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The quality of the delivery service is a crucial asset for an e-grocer to create and maintain a loyal customer-base. With the rapid market growth of e-grocers over the last decade, there is an urgent need for e-grocer specific routing systems. Although stochastic and dynamic routing models are studied for a wide range of applications, e-grocer specific models are missing in the literature. This paper investigates the concept of flexible deliveries, which introduces differentiated time window sizes. This creates the possibility for real-time re-optimization of the sequence of customers in a trip in order to improve the on-time delivery performance. The potential of flexible deliveries is investigated by means of computational experiments in which historic trip instances from the Dutch e-grocer Picnic are used. It is shown that, when re-optimization is activated, on-time delivery performance is improved and this benefit is significant when flexible deliveries represent at least 10% of the deliveries. When 10% of the deliveries are flexible, the number of late deliveries can be reduced by up to 18% and the number of extreme late deliveries $\mathbf{(\geq\ 15\ {min\ late})}$ up to 27%. This improved on-time delivery performance comes at the cost of a maximum of 2% increase in the average time spent per delivery.

For a class of delay-tolerant networks, it is assumed that the future topology changes are predictable and therefore, it is possible to find paths for data bundles by traversing the contact graph. This is the method used by Schedule-Aware Bundle Routing through Contact Graph Routing and the reward shaping method proposed for the Cognitive Space Gateway (CSG). In this work, the performance impact of anticipated contacts that randomly fail to occur is investigated. A queuing model is first formulated to gain insight into the end-to-end bundle delivery performance drop compared to the optimal performance. Then, experimental results provide further indication of the performance impact using an emulated space DTN environment. While a contact miss event can be hard to predict or to avoid, bundle re-routing is investigated for CSG routing as a possible mitigation method and is experimentally tested.

In this paper, results from an experimental study of the end-to-end bundle delivery performance of two concurrent flows transmitted over a delay-tolerant network are presented. The study compares the Contact Graph Routing algorithm, which is the foundation of the CCSDS standard Schedule Aware Bundle Routing and the Cognitive Space Gateway that has been recently introduced as a cognitive networking alternative to the problem of space bundle routing. Both algorithms make non-cooperative, dynamic routing decisions for bundles at each step based on a similar utility, which is formulated as the minimum expected bundle delivery time to the destination. The study aims to find how well each algorithm plays the routing game under different conditions. The experiments were carried out on a laboratory testbed with emulated Earth-Moon communication conditions and included evaluation cases with a permanently connected substrate and a substrate that is being affected by regular link disruptions. Also, the flows were evaluated with cases where their shortest path either overlap or not. The results indicate that both routing approaches play a coherent game achieving fair performance for both flows, but with the CSG achieving better performance than the CGR approach.

Goods distribution management is an extremely important link for logistics units.Especially for enterprises or units that have a large number of distribution needs, effective and immediate control of the cargo trajectory is a very difficult problem.At present,in order to strengthen delivery performance. More manpower and equipment must be invested to digest a large amount of goods.Although there is enough manpower in the warehouse to complete most of the inventory, distribution.But after leaving the warehouse, often only one or two people are left to be responsible for remote multi-point distribution.Barcode labels are the mainstream system used by Logistics industry.Although it can improve part of the efficiency, it can only replace part of the human.In fact, it is impossible to control all logistics status in time and accurately. In the end, we still need to rely on manpower and work experience to reduce the error rate.This paper combines the recording and management functions of RFID, GPS, WiFi module and database server.Grasp the important information such as the corresponding time, location and cargo list (time, place, object) in the process of goods distribution, and after comparing with the itinerary arrangement, achieve the effect of distribution itinerary management and real-time automatic inspection.

LTE based Cellular Vehicle-To-Everything (C-V2X) allows vehicles to communicate with each other directly without the need for infrastructure and is expected to be a critical enabler for connected and autonomous vehicles. V2X communication based safety applications are built on periodic broadcast of basic safety messages with vehicle state information. Vehicles use this information to identify collision threats and take appropriate countermeasures. As the vehicle density increases, these broadcasts can congest the communication channel resulting in increased packet loss; fundamentally impacting the ability to identify threats in a timely manner. To address this issue, it is important to incorporate a congestion control mechanism. Congestion management scheme based on rate and power control has proved to be effective for DSRC. In this paper, we investigate the suitability of similar congestion control to C-V2X with particular focus on transmit power control. In our evaluation, we include periodic basic safety messages and high priority event messages that are generated when an event such as hard braking occurs. Our study reveals that while power control does not improve packet delivery performance of basic safety messages, it is beneficial to high priority event message delivery. In this paper, we investigate the reasons for this behavior using simulations and analysis.

The Cellular Vehicle-to-Everything (C-V2X) technology has been specified to support V2X communications in Long Term Evolution (LTE) and beyond cellular networks. Whereas Release 14 specifications mainly target the sidelink support of periodic cooperative awareness messages (CAMs) exchanged among vehicles, 3GPP is currently discussing C-V2X enhancements to enable new features in 5G Releases 16 and 17, among which the support of asynchronous messages like Decentralized Environmental Messages (DENMs). Such type of messages can be generated upon an accident or other hazardous events on the road, but also to allow a vehicle to join/leave a platoon. They need to be promptly and reliably disseminated to guarantee safe driving. In this paper, we analyse the DENM delivery performance over the sidelink, when considering the C-V2X autonomous resource allocation scheme, also known as Mode 4 in Release 14 and as Mode 2 in the next Releases. The suitability to DENMs of the resource selection algorithm specified for CAMs in Release 14 is first investigated, before analyzing some suggested parameter adjustments for Release 16, and elaborating on their most suitable tuning under different simulation settings.

This study shows readers an optimized power delivery impedance using RC plane termination. We look at plane termination and its application on an electrically dense production board where remote capacitance from the termination elements alone is enough to keep voltage fluctuation loads within specifications. The paper shows how plane termination can effectively be used to mitigate plane resonances at mid to low frequencies. Measured and simulated data on a high-current memory power rail within a large computer board are used to prove that several bypass capacitors can safely be replaced by plane termination components, allowing for a significant increase in system density. We then show how these termination elements can be further optimized to improve power plane impedance.

UAE is among the fastest-growing e-commerce country in the Middle East, and this provides ample growth opportunity to the last mile parcel/courier delivery companies. Yet, the sector struggles to achieve its objective of providing exemplary customer satisfaction and minimizing the operating cost. This paper explores the challenges faced by the logistics companies engaged in the last mile delivery of e-commerce parcels. Firstly, in most countries of the Middle East including UAE there is” No descriptive delivery address “available, which means no postal codes or Zip codes of the final delivery point and secondly, most of the logistics provider still operate on legacy driven semi - digitized track & trace systems for the transportation and customer relation management. This leads to the poor last-mile delivery performance of the logistics provider ultimately impacting the e-commerce brand reputation. To increase efficiency, there is a need to embrace innovative technologies and integrate the supply chain key partners. Based on the exploratory study, we suggest a conceptual framework based on integrated technologies like GPS, IoT, and TMS which will provide the delivery operator with real-time visibility, reliability, and a smart last-mile solution. The unique three technology blend will provide full digitization of the core operations, seamless co-ordination with multiple sources, data-based decision making and real-time visibility.

The primary goal of distribution networks is to transfer uninterrupted power supply between utility and utility customers with greater quality. The control of real power transmission loss and maintaining the bus voltage within the permissible range are considered to measure the quality of power delivery by the utility. In recent years, strategies such as reconfiguration, capacitor placement and Distributed Energy Resources (DER) placement are practiced for improving the power delivery performance of the distribution system. Furthermore, the placement of Flexible AC Transmission System (FACTS) devices such as series and shunt controllers are also used for line and load compensation in the power system for performance enhancement. In this research work, the performance analysis of three types of the FACTS devices such as Static VAR Compensators (SVC), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) have been carried out with the support of hybrid Fuzzy-Flower Pollination Optimization Algorithm (FFPOA). The FPOA identifies the optimal size and location of suitable FACTS devices in a distribution system for optimizing the Total Operating Cost (TOC) and bus voltage improvement. In addition, heuristic fuzzy is integrated for the effective handling of the multi-objective problem on distribution system optimization. The efficiency of the proposed hybrid algorithm is validated with the three standard benchmark test cases.

Edge caching is one of the promising solutions to fulfill the demands in future wireless networks. Considering a multi-cell multi-user massive MIMO (multiple-input-multiple-output) system, in this paper, we analyze average success probability (ASP) measure under the assumption of maximal ratio transmission (MRT) precoding. With respect to caching, ASP represents a measure of both the content placement and delivery performance. For the networks, where the base stations (BSs) and users are distributed as homogeneous Poisson point process (PPP), we show that for interference-limited systems, ASP is independent of the densities of both BS and users, concluding that the evaluation of a given caching scheme depends on the threshold chosen. Simulation results are presented to show the above relations.

Message delivery in opportunistic mobile networks is a challenging task since the network topology constantly changes and end-to-end paths can hardly be sustained. Epidemic routing forwards a copy message to each contacted node to achieve a high network delivery performance; this however easily burdens the network nodes with high traffic load, quickly depleting the node’s resources, e.g. power and storage, and finally degrading the network delivery performance. This paper proposes an interest-based Epidemic that improves Epidemic to be a content-aware forwarding by taking message content, node interest, and node community into consideration. Using simulation, driven by real human contact datasets, we investigate the performance of the proposed algorithm compared with Epidemic (content-oblivious) and Direct Transmission (content-aware), in terms of total delivered messages, average convergence time, and total relayed messages. Simulation results show that Epidemic-Interest outperforms Direct Transmission in terms of total delivered message and average convergence time. Moreover, compared with Epidemic, it can reduces the transmission cost while keeping the total delivered messages as high as Epidemic’s; however, it increases the convergence time beyond that of Epidemic.

We propose a novel proof of delivery (PoD) mechanism to incentivize peers to participate in peer-to-peer content delivery services in a secure and effective fashion. By analyzing transaction records on a blockchain, the PoD can detect a fraudulent peer which does not pay for its content retrieval. Moreover, the PoD can enhance the delivery performance of a trustworthy peer by substantiating the notion of a trust window, whose size is proportional to the trustworthiness of a peer. That is, a trustworthy peer will experience enhanced delivery performance while a fraudulent one will be isolated from the network, which is checked by simulation results.

Increasing productivity while maintaining quality is a challenge for most managers. If this is to be done with available manpower and equipment, the task is further complicated. The process becomes more challenging with the gaps in reliable measuring tools defined for public sector performance with increasing bureaucratization. Out of the few options available to address this problem, work-flow reengineering can be considered as one option that could have an impact on productivity. This case study research was carried out on a workshop that has practiced a routine workflow with traditional practices interwoven with redundant tasks. The process has a negative impact on delivery performance, product quality and accountability of the workers. An information black-box could be observed. The research study was carried out to investigate the present status of the engineering workshop and propose a workflow re-engineering for the workshop. A qualitative study, comprising of six indepth interviews and two focus group discussions, were conducted in order to gather primary data and secondary data from office records were used to fill in gaps. Data analysis was done through transcribing, coding, and categorising the data to identify general process flow. Thereafter, Key Performance Indicators (KPI's) were developed to measure the efficiency of primary operations at various levels. Finally, based on the anlaysis, a re-engineered process model was proposed to improve the job-shop productivity.

The mobile opportunistic networks (MONs) aim to realize the data delivery in challenged network environment that is characterized by intermittent connectivity and dynamic network topology. To address the routing problems in MONs, a connectivity probability based spray and wait routing algorithm (CPSWR) is presented. First of all, in view of the data sending and receiving status of nodes in MONs, a three-dimensional connectivity matrix for network nodes is constructed. Then, CPSWR calculates the connectivity probability between nodes for the next time slot based on connectivity matrix. Finally, the spray process is guided by the connectivity probability. In CPSWR, the packet replication only takes place between nodes with high connectivity probability to improve the effectiveness of data forwarding. The simulation results demonstrate that compared with traditional spray and wait routing algorithm (SWR), CPSWR can reduce inefficient data forwarding and improve packet delivery performance.

In this paper a system is proposed whereby a group of small and inexpensive LEGO robots cooperate autonomously to transport a relatively much larger object to a specific location. In achieving this objective, the robots were able to generate a formation, recognise and locate neighbouring robot positions, control their motion and coordinate as a team in order to move the object to a set target location. Cooperative transportation algorithms were implemented, tested and evaluated on a physical setup, including pushing, caging and grasping. Results demonstrate that any desired formation shape can be generated and maintained without distance or orientation constraints. Caging and grasping algorithms yielded an accurate delivery performance, exhibiting reliability across different scenarios.

Engineering-To-Order (ETO) is a production approach where manufacturers design, engineer, and assemble a unique product based on specific customer requirements. In this type of operation, manufacturers are faced with many uncertainties in the supply chain and design process, which often lead to unreliable delivery time and high total cost. This paper proposes a project management approach to cope with the uncertainties in the ETO supply chain by allocating some buffer times within the supply chain network. Particle Swarm Optimization (PSO) is used to find the optimal configuration of the buffer time within the supply chain. The result shows that the buffer time optimization method can help manufacturers to keep a high level of on-time delivery performance (95%) while keeping the total cost minimum.

Large corporates and government institutions tend to have multi-year service engagements with IT-vendors who provide various kinds of infrastructure and management services including software, servers, IT - security, facility management etc. The engagements are formalized as legal contracts. Given the legal binding and the natural expectation to renew, there is a need to constantly monitor the performance against the stated objectives. Typically, a principle investigator or an account executive hold the responsibilities of deliverables and perform corrective actions as and when required. Given the complexity of the language and various variables involved, it is extremely difficult to proactively detect performance degradation. In this paper, we introduce the notion of contract health and describe Fitcon - a contract mining system that detects service level agreements from contracts, tracks the delivery performance against them and predicts the health of long term contracts. Fitcon is intelligent as it reads these natural language legal contract documents to set its monitoring goals. Fitcon tracks key parameters of the contract and predicts their performance. Comprehensive experiments on real contracts and corresponding past performance data are conducted to test the effectiveness of the system. We test our system across 11 large engagements and the analysis shows promising results. To the best of our knowledge, Fitcon is first such system that has been deployed into production for large scale contract health determination and prediction.

The topic of blockchain has been inundated with the fanaticism of cryptocurrencies' enthusiasts in recent years. In fact, the theory of blockchain and technologies behind are more worthy to be discussed to bring revolution to nowadays business operation. In this paper, we conduct an interdisciplinary study on business logistics as well as the cutting edge information technologies. We discuss the ongoing projects of blockchain in business industry, and then we propose our assessment model and blockchain framework to seek an enhanced metric for delivery performance with real-time feature and higher accuracy. Based on the our analysis, we identify the shortcomings of traditional assessment on delivery performance in contemporary global supply chain management and we discuss the potential benefits brought by adoption of our proposed framework.

Vehicular ad-hoc networking (VANET) is an effective and green approach to supporting the communication requirements of networked electric vehicles (EVs). This paper studies trajectory-based infrastructure-to-vehicle (I2V) message delivery over VANETs, whose operations can be summarized as follows: 1) the server first selects an appropriate roadside unit on the destination vehicle's subsequent trajectory as the rendezvous point (RP); 2) the server then transmits the message to the RP and expects that when the vehicle passes through the RP later, the message can be delivered from the RP to the vehicle. Such existing protocols commonly assume that vehicles move strictly following the suggested routes, without detours. Consequently, the delivery performance can be overestimated. This paper proposes an analytic model to investigate the impacts of vehicle detours on trajectory-based VANET I2V message delivery. The model derives the probability that a destination vehicle will pass through its RP, and thus receive the message successfully, considering the fact that the vehicle may detour during the movement. We validate the analytic model against simulation. Finally, several numerical examples are presented to quantitatively show how the message reception performance is affected by vehicle detours.

The article presents the results of experimental study, conducted to determine UAV ad hoc network parameters providing the probability of link connectivity guaranteeing PDR (Packet Delivery Ratio) not less than 90% in the given area. For the research purpose the network structure imitation model was built in ns-2. OLSR is used as a routing protocol to discover paths between the nodes. The dependence of the nodes connectivity on such network parameters as the nodes number and transmission range is determined.

This paper proposes, describes and analyses two innovative approaches which make use of the Multipath Transmission Control Protocol (MPTCP) and its multiple flows for prioritised AR/VR content delivery. The first approach involves delivery of prioritised data using a fixed subflow and therefore establishment of a virtual private channel (VPC) for sending data. The second approach introduces a novel QoS on-the-fly (QoSF) algorithm that evaluates all subflows' delivery performance and dynamically selects one of them to deliver the prioritised content with the lowest latency. Both algorithms are assessed in single-homed and multi-homed configurations and present different performance improvements in comparison to the classic MPTCP. While QoSF algorithm demonstrates the best performance in scenarios where the delivery latency variation between sub flows is high, the VPC algorithm has the best results in scenarios where this variation is smaller.

Vehicular Ad-hoc Networks (VANETs) have emerged as a promising approach to support communications between autonomous cars and the accompanying intelligent transportation systems. Research in this area, namely vehicle-to-everything (V2X) communication, is focusing on two types of technologies: dedicated-short-range communication (DSRC) and advances in cellular networks. However, both technologies have limitations in performance, especially in high-density vehicular scenarios. In this paper, we propose a cognitive protocol translator capable of translating between LTE Sidelink device-to-device (D2D) and IEEE 802.11p DSRC packets, allowing interoperability of the two technologies. We propose this hybrid architecture as a potential solution to address the challenge of interoperability. We demonstrate through simulations the feasibility of our solution and show how the protocol converter has no adverse affect on packet delivery performance of the component DSRC and LTE ProSe technologies.

Opportunistic mobile social networks (OMSNs) exploit human mobility to physically carry messages to the destinations. Routing algorithms in these networks typically favour the most popular individuals (nodes) as optimal carriers for message transfers to achieve high delivery performance. The state-of-the-art routing protocol BubbleRap uses a cumulative moving average technique (called C-Window) to identify a node's popularity level, measured in node degree, in a time window. However, our study found that node degree in real-life OMSNs varies quickly and significantly in time, and C-Window moreover slowly adapts to this node degree changes. To tackle this problem, we propose a new method of node degree computation based on the Kalman-filter theory. Using simulation, driven by real human contact traces, we showed that our approach can increase BubbleRap's performance, in terms of delivery ratio and traffic (load) distribution fairness.

Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation.

Social-rank-based forwarding algorithms favour the most popular nodes as the most likely relay nodes to deliver messages to the destinations. When these strategies are able to deliver messages with a high success rate and a low delay in social opportunistic networks (SONs), this however creates unbalanced load distribution, where the most popular nodes carry a much heavier burden compared to others. In this paper, we analyze the efficiency and fairness trade-off of social-rank-based forwarding strategies in SONs. Initially, we investigate the node popularity distribution in real-life SONs. We confirm that the node popularity is power-law distributed, with the existence of a few hub nodes that have many connections with other nodes and therefore are much popular in the entire network. Subsequently, we apply a social-rank-based forwarding algorithm on these human-centric networks. Moreover, we perform two distinct scenarios as follows. In the first scenario, we consider absolute delivery efficiency and examine the impact that hub nodes have on the network delivery performance. We show that these nodes enable the network to deliver messages with a high probability in a low delay; however, this consumes much resources on the central nodes. In the second scenario, in contrast, we consider the absolute fairness of resource allocation across the network nodes. We confirm that maintaining this fairness significantly degrades the network delivery performances.

Numerous materials are transported inside a hospital, which mostly relies on human porter. Mobile robots have been developed to substitute such human effort. These robots are able to navigate autonomously within a hospital. To estimate the number of robots to be obtained by a hospital, this paper introduces a simulator which enables comparison of the delivery performance according to the number of robots. The result shows the delivery performance by differing the number of robots which helps the management group to decide how many robots to afford by reviewing the performance report. One example of a delivery environment is tested and the result shows the estimation of delivery performance.

The latest increase in number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources, putting pressure on the existing network infrastructure. Dense Heterogeneous Networks (DenseNets) are a promising solution to this problem, supported by various individual networks and different technologies deployed within the same area. In such networks, mobile users face an important choice of what individual network to connect to in order to balance energy saving and delivery performance. This paper proposes an Energy saving-focused Mobility-Aware Network Selection (EMANS) algorithm which provides a good trade-off between energy consumption and resulted quality when delivering video content. EMANS includes an adaptive method to adjust the delivered video stream bitrate based on the available network resources such as it maintains good user perceived quality levels. Furthermore, EMANS reduces the number of handovers in comparison with other state-of-the-art approaches.

Advances in the Through Silicon Via (TSV) process have placed performance demands on gas delivery including higher levels of speed, precision, accuracy and repeatability of critical reactive gases, all driven by demands for high etch rate, increased uniformity, small scallops and better profile control. MKS pulse gas delivery mass flow controllers (Pulse MFCs) have been developed in order to solve these TSV etch challenges. Its mole based pulse gas delivery method provides unprecedented rapid pulse gas delivery performance which reduces the cost of the TSV process.

In Opportunistic Networks, information forwarding takes place when nodes are in communication range. Absence of knowledge on network topology prior information forwarding poses a compelling challenge in Opportunistic Networks. In these networks, disconnected mobile peers use mobility to opportunistically and dynamically connect to each other in order to disseminate heterogeneous information towards the intended destination. Flooding disseminates information to all nodes in range. Hence, it achieves high delivery performance. However, this form of dissemination congests the network and create considerable amount of network overheads. In this paper, a social-based neighbor selection protocol is proposed to enhance the heterogeneous information dissemination through peer-to-peer interaction in Opportunistic Networks. Neighbor is selected based on social structure formulated by Frequency in Range, Frequency of Interaction metrics and Heterogeneity of information. A high valued metric peer from the list of candidate peers is selected as potential forwarder to disseminate information. The delivery performance impact of our protocol on information dissemination was investigated and has been analyzed in customized simulation tool. Experimental results showed that, the proposed neighbor selection protocol achieved delivery performance close to random selection in flooding and reduced 88.25% push overhead incurred during dissemination.

In 2013, the Airspace Operations Laboratory at NASA Ames Research Center conducted a humanin-the-loop simulation that examined the feasibility of applying a number of Next Generation Air Transportation System (NextGen) solutions to complex arrival operations in and around the New York metroplex. The delivery of arrivals to Newark Liberty International Airport (EWR) was the focus of this simulation, which involved extending the Terminal Sequencing and Spacing (TSS) scheduling capability to precisely schedule arrivals to intersecting runways 22 Left and 11. An important enabler for the concept was the availability of a dependent runway scheduler that was able to coordinate arrival times between aircraft landing on intersecting runways. At the time of the study, there was no functionality within the TSS scheduler to automatically create the dependent runway schedules. Instead, a Traffic Management Coordinator (TMC) manually created a de-conflicted schedule, which allowed for the concept to be tested as well as provided valuable insight into the tool requirements for a dependent runway scheduler. Throughout the course of preparations for the simulation, the individual serving as the TMC developed a number of strategies and procedures for manually adjusting the Scheduled Time of Arrival (STA) of the EWR arrivals in order to ensure that adequate spacing was provided between runway 22L and 11 arrival pairs. This paper describes the strategies and procedures that were developed and details how they were successfully applied during the simulation. Results will also be presented that shed additional light on exactly how the schedules were manipulated and their impact on delivery performance and safety. Ideas for additional TSS enhancements and next steps, based on participant feedback, will also be presented.

This paper analyzes the impact of the just-in-time(JIT)practices on manufacturing enterprises operational performance (quality performance, cost performance, flexible performance, and delivery performance), as well as the moderating effect of the relationships between JIT practices and enterprise operations performance under the cost leadership strategy. Then use the 687 manufacturing enterprises data of the international manufacturing strategy survey (IMSS-V) to do hierarchical regression analysis, we study the impact of JIT practices on the enterprises operational performance. The results show that: JIT practices have a positive impact on enterprise operational performance; cost leadership strategy play a moderating role for JIT practices, quality performance and cost performance, the flexible performance and delivery performance do not play the moderate role.

In this paper, we present an open platform of content delivery networks (CDNs), namely vCDN, by which a wide range of delivery strategies can be dynamically deployed via a centralized controller and a pool of geographically dispersed cloud resources. A control application can be written for representing a specific strategy w.r.t required scale, responsiveness, and security in content delivery, and then translated into a virtualized delivery network, a form of an overlay network on cloud storages and virtual machines. In doing so, we explore the unique combination of named data networking (NDN), software defined networking (SDN), and distributed monitoring so as to achieve to a certain extent programmability in content delivery. Different from traditional CDNs, the strategy of content delivery in vCDN is not statically fixed but can be tailored by each service provider via the deployment of its own control application. Throughout a few applications running on our test bed at 7 geographical locations with multiple cloud resources, we demonstrate the feasibility of the programmable CDN and the delivery performance enhancement by inter-cloud network route managements.

This paper aims to present the system of Fraudulent Detection for Steel Logistic SME Business on Cloud Services. The system is developed based on the traditional steel logistic business management which is deployed on the cloud computing system. The study takes into account the concept of the Logistic Management in which it is started from analyzing the customer order processing and to the end process of the accounts clearing. The fraudulent detection is implemented to track all the logistic process. The survey of conceptual framework evaluation shows the significant efficiency between two kinds of business management models. Furthermore, the results of the evaluation also demonstrates that the positive feedback from all officers is greater than 80%. After implementing the success Fraudulent Detection System on the actual management, the results of the factors influencing the adoption of sufficient cloud services for steel logistic SME Business shows that the Delivery Performance Percentage (DPP) and the Truck Maintenance Performance can be classified into three levels: highest, medium and lowest of the delivery performance and the truck maintenance performance.

Opportunistic networking targets networks where end-to-end communication paths between nodes may not exist and it becomes important to seize opportunities to forward data to neighbouring nodes in the hope that it will eventually be propagated to the destination. Forwarding algorithms must address the trade-off between delivery performance (success ratio, end-to-end delay) and resource consumption (memory and bandwidth). Incorporating information about the contact pattern between nodes helps to distinguish nodes based on their forwarding capabilities. Aggregating contact events into weighted graphs, and thus forming a contact graph, enables us to detect communities and learn about the neighbourhoods of nodes. In this paper we incorporate this learning into a contact graph-based forwarding algorithm that makes decisions based on network communities. We compare the performance of this algorithm to existing forwarding schemes using simulations built on two real word contact-trace datasets that have distinct characteristics.

Vehicular ad hoc networks (VANETs) are viewed as an effective way to disseminate content among vehicles on the road. While most of the current research efforts in VANETs focus on improving the packet delivery performance, only limited work aims to provide cost-efficient solutions for content downloading. This paper proposes a novel approach for the vehicles to download a common content in a cost-efficient way. The basic idea is to stimulate the vehicles to download the content cooperatively in mutually disjoint coalitions. To study the cooperation among the vehicles, a coalitional game model is proposed. Moreover, a distributed coalition formation algorithm is designed to obtain a stable coalition structure and minimize the total communication cost. Numerical results show that the proposed content download approach can achieve a considerable communication cost reduction compared with the non-cooperative case.

Hop count is an important parameter for routing strategies in wireless network. Particularly in the opportunistic networks, due to the intermittent connectivity between mobile nodes, it imposes great impact on the delivery performance in terms of delivery ratio and average latency. In the previous works, there are papers either claimed that two-hop is enough, or claimed that more hops increase the delivery delay, and also there is the attitude that more hops reduce more delivery delay. All of those conclusions are well justified under their specific assumptions and conditions, but the factors arousing such different attitudes towards multi-hop benefit have not be well investigated. In this paper, we establish a framework to analyze opportunistic multi-hop relaying benefit based the concept of Good Relay Ratio (GRR), and discuss the factors that impact max-benefit hop count in three typical multi-hop forms. Finally, we make a set of realistic trace-driven simulation to verify the analytical results. It is expected that the framework and result implications are meaningful to the routing strategy design in opportunistic networking.

High performance computing (HPC) platform power delivery simulation is typically based on state average mode for simplicity. However, it is not able to capture the dynamic ripple noise and non-linear effect of output voltages, as well as the phase information. The correlation between state average model and lab measurement often lacks accuracy in low to middle frequency range. As a result, platform level power delivery performance assessment has to rely on hands-on measurements to fine-tune the board in order to meet platform specifications, which poses a stringent challenge to product readiness and development cost. To alleviate this problem, single shot design with accurate predictive switching VR modeling to achieve performance and cost balance has been identified as the goal. In this paper, we demonstrated a switching VR modeling methodology with accurate correlation and prediction results using On Semi voltage regulator (VR) model and Intel Romley platform customer reference board (CRB). This methodology applies to other platform simulation with different VRs and motherboards, and can be proliferated to external customers to improve their product design.

Enterprises often maintain many IT applications to support their business. Application Management Services (AMS) aim to maintain high levels of service quality and availability by restoring normal application service operations and minimizing negative business impact. In this paper, we present the AMS Analytics System for improving the productivity and quality of delivery for AMS practices. Issues regarding IT applications are formally referred as IT incidents or tickets, which are an important vehicle for measuring quality of AMS. IT incident ticket analytics, an important component of of the analytics system, measures workload variability, resource productivity and delivery performance using algorithms from statistics, queuing theory, data clustering and signal processing. The AMS Analytics System provides a standardized, integrated analytics platform supporting AMS delivery. It is built on a Web platform using a set of standard open stack software, enhanced with advanced analytics. Since its initial release, we have applied the AMS Analytics System to several dozens of real-world enterprise users, receiving very positive feedback.

Quality management or six sigma management mainly focuses on internal organization. This paper carries forward six-sigma metrics, especially nonconforming rate into supply chain domain to develop a framework which evaluates the delivery performance of the entire supply chain and its all related processes. The proposed framework is different from other conventional performance evaluation methods. The proposed framework uses the sample data to calculate the nonconforming rate. Futhermore, the corresponding sigma level for the delivery performance can be obtained through the standard normal distribution table. The model presents a unique opportunity in identifying the current status of supply chain, taking corrective actions and increasing the customers' satisfaction. A case study is given to validate the proposed framework, and which is about a multinational home applications manufacturer's supply chain network.

The term Sink Powered Underwater Acoustic Sensor Networks (SPUASN) refers to a special configuration within the recent Remotely Powered Underwater Acoustic Sensor Networks (RPUASN) paradigm, where the data sink supplies energy to battery-free sensors that constitute the network. In this paper, we introduce the Cross Layer Power Adaptive CSMA/CA (X-PACCA) protocol for SPUASN. Fully free from energy constraints of traditional protocols, X-PACCA integrates MAC, network, and transport layer functionalities. Packet relaying/routing is based on CSMA/CA backoff window size adjustment according to harvested power levels at nodes. End-to-end reliability is enhanced via acknowledgments sent by the sink. Congestion is avoided via prevention of redundant packet forwarding. Neither global network information nor synchronization is required at nodes. It is shown that, with appropriate selection of protocol parameters, X-PACCA achieves low end-to-end latency and high packet delivery performance.

This paper reports on modeling of the power delivery in TSV-based 3D systems, i.e., vertically integrated ICs with uniformly-distributed high-aspect-ratio through-silicon vias (TSVs). The voltage drop and di/dt noise are modeled to evaluate the impact of TSV density and TSV aspect ratio (AR). The frequency dependency of TSV parasitics is modeled with fullwave EM simulator, and the performance of the power delivery network (PDN) is analyzed in the frequency domain. PDNs with low-AR and high-AR TSVs are compared for trade-off analysis in terms of power delivery performance and TSV area occupation, which is critical for the design of TSV-based 3D power delivery.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring further development and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses with respect to a complete stack of protocols at large scales. Specifically, very little is known about (1) When, where, and under what kind of circumstances packet losses occur. (2) Why packets are lost. As a step towards addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined casual graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power adhoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments.

Opportunistic networking is rapidly emerging as a suitable communication paradigm to characterize social networking applications and contents dissemination. However, due to the sparsity of the opportunistic network, it is expected that the considered mobility model will play a crucial role in the delivery performance of the opportunistic system. Accordingly in this paper we present an analysis of the impact of different conventional mobility models on the delivery performance of opportunistic networks. In particular, we focus on the estimation of the packet delivery delay and the power consumption associated to the delivery procedure which are the two key metrics to be traded-off according to the application requirements.

In this paper we present a transmission scheduling protocol for data-gathering Wireless Sensor Networks (WSN). We focus on the delivery performance in term of overall Packet Receive Ratio (PRR) at the sink node. In order to reduce the energy consumption and increase the overall delivery performance, elaborated scheduling algorithm is applied to the transmission and relaying of data packets, based on a beacon-broadcasting global synchronization. We tested the protocol using CiNet nodes, which is built on IEEE 802.15.4 compliant radio modules CC2420 and Jennic JN5148, and obtained significant delivery improvement in multihop scenarios. This protocol is suitable for energy-hungry data gathering wireless sensor networks in which the sensor nodes use a short time window for radio activities in order to save energy.

Delay tolerant networks (DTNs) rely on the mobility of nodes and sequences of their contacts to compensate for lack of continuous connectivity and thus enable messages to be delivered from end to end in a “store-carry-forward” way. Since each node may also need to deliver out its locally generated message, in addition to carrying and forwarding messages for other nodes, the node may become more willing to forward its own message rather than that of others when it encounters some node. This kind of selfish behaviors may become much more significant when the nodes are operating under both QoS requirements (e.g., delivery delay requirements) and energy constraints. In this paper, we analytically explore how this kind of selfish behaviors will influence the delivery performance of the two-hop relay in the challenging DTNs. In particular, a continuous time Markov chain-based theoretical framework is developed to model the complicated message delivery process. With the help of the theoretical framework, closed-form expressions are further derived for both the expected delivery delay and the expected delivery cost, where the important node selfishness issue is carefully incorporated into the analysis.

Opportunistic networking involves forwarding messages between proximate users, who may or may not know one another. This assumes that users are willing to forward messages to each other. This assumption may not hold if users are concerned about using the opportunistic network service. One such concern may be due to privacy; for instance, users' locations may be leaked. A privacy-concerned user may therefore disable their mobile device's opportunistic-networking features at various times, to preserve their privacy. This paper studies the impact of location privacy concerns on the performance of an opportunistic network. Using data from a real-world location-aware user study to develop a privacy model, we conduct trace-based simulations of various opportunistic routing protocols with two real-world traces. We find that users' location privacy preferences may potentially reduce the delivery performance of an opportunistic network to zero. Postprint

The literature suggests a growing interest in the application of network analysis in supply chain management. However this has been at the organizational rather than the process level. We believe there is value in applying such analysis to internal processes in supply chain networks. This study uses network analysis techniques to investigate the Stewart [8] framework for excellence in supply chains which considers the impact of delivery performance, flexibility and responsiveness, logistics cost and assets management. We analyze traditional process flow charts as a network in which nodes represent processes, and links between processes have values associated with dimensions of excellence. The supply chain is analyzed and viewed using measures of centrality and clustering related to the dimensions of excellence, and compared with traditional perceptions of the same processes which are more related to time criticality. Overall, the study indicates that traditional means of managing supply chains are not only unfocused in terms of excellence, but are compromised through inability to recognize the importance of various process groups, with a serious mismatch of resourcing as a result. The study provides early findings supporting the future development of a methodology to better manage supply chains using network analysis, in particular to better prioritize resources on critical processes.