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In this article, we consider a network with a hybrid access point (HAP) and radio frequency (RF)-energy harvesting wireless devices. The HAP is responsible for charging these devices and receiving their data. Our problem is to select a set of devices to transmit in each time slot so as to maximize a given reward over a planning horizon. In contrast to prior works, we consider the challenging case whereby the HAP has imperfect channel state information (CSI) nor information about the battery state of devices. We also consider nonlinear RF-energy conversion rates and battery leakage. We propose a cross entropy approach to identify the best set of devices to select in each time slot over random channel gains. In addition, we propose a fast Gibbs sampling approach, called Gibbs $^+$ , that incorporates a novel step to evict noncompetitive devices. We compare our solutions against random pick, round robin, original Gibbs sampling, and perfect information selection (PIS). Our results show that CE and Gibbs $^+$ algorithm produce a higher reward than RR, RP, and OGS. Advantageously, our CE and Gibbs $^+$ algorithms, respectively, achieve $\text{99\%}$ and $\text{98\%}$ of the theoretical maximum throughput as computed by PIS, which has perfect information.

This article considers an energy-harvesting wireless sensor network (EH-WSN) where nodes have a dual alternative battery system. We aim to design a routing protocol that transmits a given amount of data to a sink using the minimum number of time slots. To this end, we first formulate the routing problem as a mixed-integer linear program (MILP) and model the charging and discharging process at each node. Moreover, we also propose a distributed multiarmed bandit-based routing algorithm (MABRA), where each node learns the best next hop(s) to forward its data based on the battery and data information of its neighbors. We study how the number of nodes that generate data or sources, solar panel area, and path exploration probability impacts the end-to-end delays computed by MILP and MABRA. Numerical results show that the end-to-end delays computed by MILP increase with the number of sources. It, however, decreases if nodes use larger solar panels. Critically, nodes experience end-to-end delays that are ${\text{55}}\%$ lower than the average end-to-end delay of a competing routing algorithm.

Future Internet of Things networks are likely to have radio frequency (RF) energy harvesting sensor devices and also solar-powered hybrid access points (HAPs) that help monitor one or more targets such as assets in a warehouse. In this setting, we outline three contributions that ensure devices’ monitor targets at all times and upload the maximum number of samples to an HAP in order to ensure high monitoring quality. First, we outline a mixed integer linear program (MILP) and use it to optimize the transmission power of HAPs and time used by devices to harvest RF energy, monitor target(s), and data transmissions. Second, we propose two heuristic algorithms to solve large problem instances. The results show that our heuristics achieve almost 97% of the optimal result computed by the MILP.

A multi-cell network that uses orthogonal frequency division multiplexing access (OFDMA) is studied here. Unlike prior works, the proposed network consists of both (i) legacy data users that do not have energy harvesting capability and have a minimum data rate requirement and (ii) radio frequency (RF)-energy harvesting devices with a minimum energy requirement. It studies sub-band allocation to users and transmits power allocation at base stations. The authors formulate a mixed-integer non-linear program and also present two heuristics to assign sub-band to base stations. Numerical results show that RF energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are ∼ 95% of the optimal solution.

In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

We consider a Hybrid Access Point (HAP) that is equipped with a Successive Interference Cancellation (SIC) radio, and it is responsible for charging Radio Frequency (RF) energy harvesting devices. In addition, the HAP assigns one or more uplinks data transmission slots to these devices. In this work, we address an important and challenging problem: determine an uplink transmission schedule that maximizes the throughput at the HAP. Unlike past works, we do not assume the HAP has perfect Channel State Information (CSI). We outline a discrete optimization approach that allows the HAP to learn the best transmission schedule. We also present a heuristic approach that allows the HAP to iteratively construct a schedule. Our results show that the HAP is able to learn a transmission schedule that has higher throughput than Time Division Multiple Access (TDMA) where each slot has one device.

This paper considers a swarm of Unmanned Aerial Vehicles (UAVs) managed by a ground station. These UAVs may experience highly varying channel gains and collisions when they transmit to the ground station. To this end, we introduce a novel Learning Medium Access Control (L-MAC) for multi-rate UAVs and equip the ground station with Successive Interference Cancellation (SIC) capability. The ground station uses L-MAC to learn a Time Division Multiple Access (TDMA) schedule/frame length that yields the highest throughput. UAVs, on the other hand, use L-MAC to learn the best transmission slot and data rate for a given frame length. Our extensive simulation results show that L-MAC achieves up to five times higher throughput as compared to the well-known Aloha protocol. Specifically, L-MAC achieves a throughput of 500 kbps as compared to 100 kbps for Aloha. In comparison, Aloha with SIC achieves a throughput of 300 kbps for the same network scenario. On the other hand, the throughput of L-MAC is as most half that of the case when the ground station has perfect channel state information. Our results also show that the frame length is always set to around 60–75% of the total number of UAVs.

Energy harvesting wireless sensor networks (EH-WSNs) form the foundation of Internet of Things (IoTs) systems. Energy harvesting nodes can be deployed strategically to monitor one or more targets such as a valuable asset. However, as these nodes rely on ambient energy sources such as solar, they experience random energy arrivals. Consequently, they may exhaust their harvested energy while monitoring a target. Therefore, network operators require a robust solution that ensures all targets are monitored continuously over some time period with a given probability. In this paper, we consider three novel robust coverage requirements; each must hold with probability ( $1-\epsilon$ ), where $\epsilon$ is the probability of failures. First, sensor nodes must not expend more than their total harvested energy over $T$ time slots. Second, the energy expenditure of each sensor node must not exceed the energy harvested in each slot . Third, the energy expenditure of sensor nodes must not exceed the energy accumulated up to the current slot. We formulate chance-constrained stochastic programs that incorporate these requirements and solve them using the sample average approximation method. We confirm via extensive simulation studies that our programs are capable of computing sensor nodes activation times that meet a given coverage failure probability.

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution.

This letter considers radio frequency (RF) energy harvesting and targets monitoring sensor nodes that are managed by a hybrid access point (HAP). We address a novel problem called maximizing data collected for complete targets’ coverage (MDC-CTC) that involves computing a schedule comprising the time used by the HAP for charging, and the time used by the sensor nodes for monitoring targets and data upload. We model the MDC-CTC problem as a linear program (LP) and use it to determine the optimal schedule that maximizes the amount of data collected by the HAP. In addition, we also propose a heuristic to generate a subset of set covers for use by the LP in large-scale networks. Our results show that the amount of data collected by the HAP is proportional to the number of sensor nodes or the HAP’s transmission power. Finally, using our heuristic along with the formulated LP yields results that are within 75% of the optimal result.

UAVs are powerful IoT components, offering sensing, communications and data analysis in the air. Recently, 3GPP TS 22.261, TR 22.862, and TR 36.777 have specified performance requirements for communications between multiple UAVs in the 5G domain. This article discusses communications reliability challenges in a UAV swarm context. Recommendations for designing an ultra-reliable communications system for UAV swarms are introduced with a focus on the software protocol stack and RF hardware. For the purpose of demonstration, we developed Easy- Swarm, an open-source UAV swarming platform that adopts the LoRa radio at the physical layer and a low-latency channel access protocol at the MAC layer. Real-life testbeds are built consisting of 10 UAVs and 20 robotic cars that produce background traffic. LoRa, WiFi and LTE networks are employed to provide broadband and cellular wireless network support. Results show that using LoRa leads to better reliability, in particular allowing for higher swarm density and longer coverage distance, than when WiFi is used. In addition, LTE provides the best reliability and latency for UAV swarms with good network connectivity.

In a rechargeable wireless sensor network (rWSN), the amount of data forwarded by source nodes to one or more sinks is bounded by the energy harvesting rate of sensor nodes. To improve sensing quality, we consider a novel approach whereby we place a finite number of auxiliary chargers (ACs) with wireless power transfer and energy harvesting ability to boost the energy harvesting rate of some sensor nodes. We formulate a mixed integer linear program (MILP) to determine the subset of nodes that if upgraded will maximize the minimum source rate. We also propose two heuristic algorithms to place ACs in large-scale rWSNs: greedy node deployment (GND), which checks every nonupgraded sensor node and places an AC next to the one yielding the highest increase in max–min rate; and one-unit energy deployment (OUED), which uses a relaxed version of the MILP to first share one unit of energy among sensor nodes. It then upgrades the sensor node with the highest one-unit share. Our results show that the max–min rate obtained by GND and OUED is, respectively, within 99.60% and 97.82% of the max–min rate derived by MILP in small networks with at most 90 nodes. In large networks with 200 nodes, the maximum gap between OUED and GND is only 0.191 kb/s. Lastly, OUED runs at least five times faster than GND.

A key approach to improve the capacity of wireless mesh networks (WMNs) is to equip routers with multiple transmissions or receptions (MTR) capability. Thus, the resulting MTR WMN has significantly higher network capacity because routers can activate multiple links simultaneously. This, however, requires an MTR link scheduler that maximizes network capacity or equivalently, one that is capable of deriving a short schedule. Henceforth, we propose period controlled pseudo time-division multiple access (PCP-TDMA), a link scheduler that allows nodes to cooperatively reduce an initial link schedule or superframe over time in a distributed manner. Routers are able to adapt the superframe size iteratively using only local information to accommodate any topological changes. This means PCP-TDMA is particularly suited for use in large-scale MTR WMNs. We have evaluated PCP-TDMA in various network topologies, and compared it against ALGO-2, a centralized algorithm that uses global topological information to derive a schedule and thus serves as a benchmark. We also compare PCP-TDMA against two distributed approaches: JazzyMAC and ROMA. The results show that PCP-TDMA achieves similar performance with the centralized algorithm in all scenarios, and outperforms the distributed approaches significantly. Specifically, in a fully connected network, the resulting superframe length of PCP-TDMA is less than $1/3$ and $1/2$ of JazzyMAC and ROMA, respectively.

This letter considers energy harvesting sensor nodes monitoring multiple fixed targets. The goal is to monitor these targets for the longest time period. A key challenge, however, is that sensor nodes have varying energy harvesting rates, meaning that the coverage lifetime of targets is limited by sensor nodes with low energy. To this end, we consider energy sharing between sensor nodes. We model the problem as a mixed integer linear program (MILP). Advantageously, our MILP models non-linear energy conversion rates. We also outline a heuristic for large-scale networks. Our results show energy sharing and non-linear energy conversion rates prolong coverage lifetime. Finally, our heuristic achieves 90% of the optimal coverage lifetime.

Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP.

This letter considers a two-tiered multi-hop RF-harvesting network comprising of wireless routers and so called eh-nodes that harvest energy from RF emitted by the said routers. Our aim is to derive the shortest possible superframe or time division multiple access schedule for use by routers, which are responsible for meeting flow and energy demands. We present a linear program to derive the optimal schedule whilst satisfying the said demands. We also outline a heuristic algorithm called Algo-TS to generate transmission sets. Our results show Algo-TS produces superframes that are at most 2% longer than the optimal solution in all tested scenarios.

This paper studies the suitability of shorted patch and CPW-feed square slot antennas for CubeSat communications. To study the effect of the CubeSat body on the antennas performance, we have simulated both antennas in the High Frequency Structure Simulator (HFSS) with and without the CubeSat body. Compared to CPW- feed square slot antenna, the shorted patch antenna achieves higher gain and wider bandwidth. We have also re-dimensioned both antennas to shift their resonant frequencies to 2.45 GHz using Quasi Newton method in HFSS. This thus enables their use in the unlicensed ISM band. The repurposed shorted patch has smaller return loss; e.g., -27.5 dB (without CubeSat), higher gain; e.g., 5.3 dBi and wider bandwidth than the repurposed CPW-feed Square slot antenna. Lastly, further enhancement in the gain of re-dimensioned CPW-feed square slot antenna shows an increase of total gain from 2 to 2.52 dB.

Multiple transmit (Tx) or receive (Rx) capability is a significant advance in wireless communications. This so called MTR capability allows the creation of wireless mesh networks (WMNs) that are ideal for use as a high-speed wireless backbone that spans vast geographical areas. A fundamental problem, however, is deriving a minimal transmission schedule or superframe that yields low end-to-end delays, with the primary constraint that routers are not allowed to Tx and Rx simultaneously. In this paper, we consider a joint routing and link scheduling approach that addresses two fundamental issues that influence end-to-end delays: superframe length and transmission slot order. Shortening the superframe length, in terms of slots, is expected to minimize the inter-link activation time while reordering transmission slots increases the likelihood that links on a path are activated consecutively. We propose two algorithms. The first called JRS-Multi-DEC uses a novel metric to minimize the load of each link while the second, called JRS-BIP, uses a Binary Integer Program approach. Both algorithms aim to minimize the overall delay and use slot re-ordering on the resulting schedule to further reduce delay. Numerical results show both algorithms are able to reduce the average end-to-end delay by approximately 50% as compared to a non joint routing algorithm.

The link schedule of a wireless network must ensure links receive sufficient transmission opportunities or time slots to meet their offered load. To date, existing link schedulers assume fixed link load. In practice, link load is likely to vary, meaning the computed schedule or superframe will have unnecessary idle times. In this letter, we propose to use stochastic programming (SP) to generate a superframe comprising of a Time Division Multiple Access (TDMA) and a random access part. Advantageously, it sizes both parts according to traffic distribution. We show how it can be used to derive a superframe for multi transmit (Tx) or receive (Rx) wireless networks. We show via numerical results the efficacy of our approach in reducing idle times and collisions given random demands.

We study the problem of deriving the shortest schedule required to forward both uplink and downlink packets in wireless mesh networks (WMNs) with full duplex capability. We derive the theoretical upper and lower bound of the schedule, and propose a novel centralized algorithm, called UDMAC, that greedily generates a schedule on a path-by-path basis and ensures nodes have sufficient antennas for transmissions, receptions, and interference cancellation. Our results show that UDMAC outperforms a state-of-the-art half-duplex scheduling algorithm by at least 60% in terms of schedule length.

This letter considers routing in delay tolerant networks whereby nodes have semi-predictable mobility patterns within a time period. We propose a mobility-based routing protocol (MBRP) where nodes construct a space–time graph dynamically. As the space–time graph may be incomplete, MBRP presents a heuristic that evaluates encountered nodes based on their recorded mobility patterns in order to disseminate a finite number of bundle replicas. Simulation results, over a service quality metric comprising of delivery, delay and overhead, show that MBRP achieves up to 105 % improvement as compared to four well-known routing protocols. Finally, MBRP is capable of achieving 50 % of the performance attained by the optimal algorithm, whereby all nodes are preloaded with a space–time graph.

Current network infrastructures are over-provisioned to increase their resilience against resource failures. Such strategies exhibit poor energy efficiency during off-peak periods. In this respect, energy aware Traffic Engineering (TE) solutions aim to maximally switch off redundant network resources when traffic load is low. However, these green TE solutions do not consider their effects on network fault-tolerance. In this paper, we first aim to quantify the effects of five recently proposed green routing approaches, namely FGH, GreenTE, MSPF, SSPF, and TLDP, on the following two reliability measures: (i) terminal reliability (TR), and (ii) route reliability (RR). Experiments using three topologies with real and synthetic traffic demands show that green approaches that switch off redundant links affect TR and RR significantly. Specifically, routing traffic through multiple paths impacts reliability less while reducing energy, especially when the paths are link disjoint. Interestingly, TLDP and MSPF have better route reliability than using shortest path (SP) routing. We then formulate a problem, called reliable energy-aware-routing (R-EAR), which aims to maximally switch-off network cables subject to link utilization as well as TR/RR requirement. We also propose an effective algorithm, called reliable Green-Routing (R-GR), to solve R-EAR. Evaluation on three real topologies shows that R-GR can save energy while satisfying both reliability and link utilization requirements. Specifically, for the GEANT network, R-GR saves up to 25.65% in energy usage without reducing RR, and saves up to 48.65% whilst reducing its average RR only by 9.1%. For the same network, our solution achieved an energy saving of 28.38% while reducing its average TR by only 8.5%.

Green networking techniques usually shut down the least utilized links and/or routers during off-peaks hours. In a prior work, we showed conclusively that these actions trigger what is known as hot-potato routing changes. These changes are well known to negatively impact the operation of the Border Gateway Protocol (BGP). Critically, they may cause packet loss due to slow BGP convergence and increased congestion. In order to overcome such negative impacts, in this paper, we propose a novel framework that allows Ingress Routers (IRs) to work collaboratively to determine the set of links that should be carrying their respective traffic to Egress Routers (ERs). IRs exchange information about their established paths with each other, and each IR determines whether to shift their existing paths to ERs to optimize a given objective, e.g., energy savings. The IRs repeat the process until convergence. Unused links are then shut down, and thereby, lowering the energy consumption rate of a network. In this framework, we study a number of key issues: first, we study the order in which IRs establish paths, namely (i) most savings, (ii) round robin, and (iii) random order. Our results show that similar performance is achieved regardless of employed method. However, in some cases, IRs that establish paths using round robin took up to 23% more time to converge than IRs that used the most savings method. Second, we study path selection metrics. In particular, IRs select paths according to (i) shortest path, where paths with the minimum number of hops are selected, (ii) longest path, where paths with the largest number of hops are preferred, and (iii) random, where paths are randomly selected. Our experimental results over five topologies show that using shortest paths is the most efficient where IRs use as little as 25% more active links than a global optimal solution. However, IRs establishing paths in a robin manner are able to utilize 5% fewer number of links than when they employ the shortest path to each ER.

Network robustness and throughput can be improved by routing each source-to-terminal (sd, td) demand d via two disjoint paths (2DP). However, 2DP routing increases energy usage despite yielding lower link utilization and higher redundancy. In this paper, we address the problem of minimizing the energy usage of networks that use 2DP. Specifically, our problem, called Energy-Aware Two Disjoint Paths Routing (EAR-2DP), is to maximally switch off redundant links while guaranteeing at least 0≤T≤1.0 fraction of all possible (sd, td) 2DPs remain on and their maximum link utilization (MLU) is no greater than a configured threshold. We first prove that EAR-2DP is NP-complete. Then, we design a fast heuristic solution, called Two Disjoint Paths by Shortest Path (2DP-SP). We have extensively evaluated the performance of 2DP-SP on real and/or synthetic topologies and traffic demands with two link-disjoint paths (2DP-L) and two node-disjoint paths (2DP-N). Our simulation results show that 2DP-SP can reduce network energy usage, on average, by more than 20%, even for MLU below 50%. As compared to using Shortest Path (SP) routing, while reducing energy by about 20%, 2DP-SP does not significantly affect the path length of each (sd, td) demand, even for MLU

Several studies have demonstrated the benefits of using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). However, these benefits are dependent on the path taken by the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multihopping to the nearest RP. The fundamental problem then becomes computing a tour that visits all these RPs within a given delay bound. Identifying the optimal tour, however, is an NP-hard problem. To address this problem, a heuristic called weighted rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP. WRP is validated via extensive computer simulation, and our results demonstrate that WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption by 22% and increases network lifetime by 44%, as compared with existing algorithms.

Broadcast is a fundamental operation in wireless sensor networks (WSNs). Given a source node with a packet to broadcast, the aim is to propagate the packet to all nodes in a collision free manner whilst incurring minimum latency. This problem, called minimum latency broadcast scheduling (MLBS), has been studied extensively in wireless ad-hoc networks whereby nodes remain on all the time, and has been shown to be NP-hard. However, only a few studies have addressed this problem in the context of duty-cycled WSNs. In these WSNs, nodes do not wake-up simultaneously, and hence, not all neighbors of a transmitting node will receive a broadcast packet at the same time. Unfortunately, the problem remains NP-hard and multiple transmissions may be necessary due to different wake-up times. Henceforth, this paper considers MLBS in duty cycled WSNs and presents two approximation algorithms, BS-1 and BS-2, that produce a maximum latency of at most $$(\Delta -1) TH$$ ( Δ - 1 ) T H and $$13TH$$ 13 T H respectively. Here, $$\Delta$$ Δ is the maximum degree of nodes, $$T$$ T denotes the number of time slots in a scheduling period, and $$H$$ H is the broadcast latency lower bound obtained from the shortest path algorithm. We evaluated our algorithms under different network configurations and confirmed that the latencies achieved by our algorithms are much lower than existing schemes. In particular, compared to OTAB, the best broadcast scheduling algorithm to date, the broadcast latency and transmission times achieved by BS-1 is at least $$\frac{1}{17}$$ 1 17 and $$\frac{2}{5}$$ 2 5 that of OTAB respectively.

Epidemic and ts variants are one main category of routing protocols in delay tolerant networks. They are known to be simple, have low latency and high delivery ratio. However, to date, virtually no work has investigated their suitability in delivering multicast bundles. To this end, we have conducted extensive and comprehensive simulation studies to answer this question. Specifically, we compare their delivery ratio, and buffer occupancy level in various multicast scenarios where nodes move according to both random way point model and a trace file containing records of student movements on a campus. Moreover, we tested the influence of key factors such as anti-entropy session, multicast group size, the number of groups and subscribers forwarding policies. We found that, achieving a high delivery ratio is dependent on nodes using anti-entropy session, relay/subscriber ratio and subscribers working as relays. Additionally, we also observed that relay nodes that use epidemic variants experience high buffer occupancy level. In particular, protocols such as epidemic with encounter count (EC) result in 100 % buffer utilization. To solve this problem, we propose EC quota, in which bundles can only be transmitted to $$k$$ relay nodes. Our experiments prove that EC quota is capable of reducing the buffer occupancy level of relay nodes by 15–100 % whilst maintaining high delivery ratios.

Wireless Sensor Networks (WSNs) have many characteristics that are attractive to a myriad of applications. In particular, nodes employ multi-hop communications to collaboratively forward sensed data back to one or more sinks. In this context, reducing the end-to-end delay between the sink and sensor/source nodes is of interest to many applications. In particular, those that require a fixed, upper bound on end-to-end delays. To this end, we focus on bounding the end-to-end delay from the sink to each source. We first formulate the problem as a Binary Integer Program (BIP). As the problem is NP-hard, this paper proposes and studies two centralized, heuristic algorithms: Tabu and Domino. The key approach used by both algorithms is to determine the minimal number of extra wake-up slots required by a given network in order to ensure the delay of all end-to-end paths is within a given bound. We conducted two sets of experiments. The first set compares BIP, Tabu, and Domino in WSNs with up to 80 nodes. These experiments serve to compare the proposed algorithms against BIP, which become computationally expensive in large scale WSNs. The results show that, compared to BIP, the number of additional wake-up times generated by Tabu and Domino are within 5 and 10 % of the optimal solution. In the second set of experiments, which evaluates the algorithms in WSNs with 100---500 nodes, the average number of extra wake-up slots activated by Domino is 13 % greater than Tabu. These algorithms have a time complexity of $${\mathcal {O}} (\alpha n^2 T + n^3)$$ O ( ? n 2 T + n 3 ) and $${\mathcal {O}}(n^3)$$ O ( n 3 ) respectively, where $$n$$ n is the number of nodes, $$T$$ T is the number of slots in one period, and $$\alpha$$ ? is the maximum number of iterations carried out by the Tabu algorithm.

Current networks are typically over-provisioned to ensure low delays, redundancy and reliability. These Quality of Service (QoS) guarantees are typically achieved using high end, high power network equipments. Their use, however, has led to concerns regarding green house gas emissions, which garnered a lot of attention recently and have resulted in a number of global initiatives aim at reducing the carbon footprint of Internet Service Providers (ISPs). These initiatives have motivated ISPs and researchers to design novel network algorithms and hardware that scale the usage or active time of a network according to traffic load. To this end, this paper considers the problem of shutting down a subset of bundled links during off-peak periods in order to minimize energy expenditure. Unfortunately, identifying the cables that minimize this objective is an NP-complete problem. Henceforth, we propose several practical heuristics based on Dijkstra’s algorithm and Yen’s k-shortest paths algorithm. We evaluated our heuristics on the Abilene network – with both real and synthetic traffic matrices and several larger random topologies with various loads. Our results show that the proposed heuristics to be effective and efficient. Moreover, our approaches could potentially reduce the energy usage of cables used in the Abilene network by up to 56.7%, assuming the traffic demands recorded on September 5, 2004.

Multicast is an important service relied upon by many applications in Wireless Local Area Networks (WLANs), which unfortunately is limited by the subscribing station with the lowest data rate. A key approach to address this problem is to employ an association control strategy to ensure stations are organized in a manner that optimizes a given objective. To this end, this paper studies the NP-hard, Multiple Multicast Sessions (MMS) problem. It proposes a novel greedy algorithm that aims to minimize the average APs load. Simulation results show that our algorithm reduces the average APs load by up to 70.4% as compared to prior approaches.

Wireless Sensor-Actuator Networks (WSANs) have a myriad of applications, ranging from pacifying bulls to controlling light intensity in homes automatically. An important aspect of WSANs is coordination. Unlike conventional Wireless Sensor Networks (WSNs), sensor and actuator nodes must work hand-in-hand to collect and forward data, and act on any sensed data collaboratively, promptly and reliably. To this end, this paper reviews current state-of-the-art techniques that address this fundamental problem. More specifically, we review techniques in the following areas: (i) sensor-actuator coordination, (ii) routing protocols, (iii) transport protocols, and (iv) actuator-to-actuator coordination protocols. We provide an extensive qualitative comparison of their key features, advantages and disadvantages. Finally, we present unresolved problems and future research directions.