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Infrastructure-less (sometimes known as ad-hoc) networking paradigm is very appealing and potentially shaping its future into almost all emerging networks (i.e., IoT, wireless sensor networks, vehicular ad-hoc networks, emergency, and tactical radio networks, etc.). However, when conventional networking protocols are used, such networks often perform poorly, mainly because of interference within the network and limited network throughput. Recent advancements in wireless communications have enabled full-duplex (FD) operation by suppressing self-interference, which can theoretically double the network throughput. However, conventional medium access control (MAC) protocols like carrier-sense multiple access with collision avoidance (CSMA/CA) favor half-duplex (HD) operation and fail to benefit from FD transmission opportunities. This article proposes a novel hybrid MAC protocol for full-duplex ad-hoc networks. The proposed MAC combines time division multiple access (TDMA) and IEEE 802.11 distributed coordination function (DCF) strengths in chains of time-slotted contention-based control frames and collision-free data frames. The aim is to fully utilize FD transmission opportunities to increase network throughput. The proposed protocol modifies request-to-send (RTS) and clear-to-send (CTS) frames in IEEE 802.11 DCF MAC to form FD-RTS/CTS frames. These frames are used to enable collision-free FD communications among neighbors. The proposed scheme mitigates conventional MAC issues like hidden-node problem (HNP) and exposed-node problem (ENP). It also allows concurrent FD data transmissions in a collision-free manner. The model is generic and can be applied to any ad-hoc wireless network. In this article, the design is applied to a single channel ad-hoc network with FD transceivers. We compared the proposed design with IEEE 802.11 CSMA/CA protocol with both HD and FD transceivers. The simulation results show a 30% gain in throughput, reduced latency, and fairness among participating wireless nodes.

Unlike commercial networks, the tactical networks drive in a critical environment and without a backbone infrastructure. These networks involve mission-critical operations that are dependent on the rapid and reliable transfer of delay-sensitive data to conduct command and control (C2). Owing to the decentralized and dynamic nature, tactical networks need to survive by maintaining seamless and simultaneous time-sensitive communication among software-defined radios (SDRs). Under mobility and dynamic network topology, link status continuously changes that cause substantial packet loss, and degrade network performance. It is challenging to maintain the connectivity between communicating nodes and find a suitable time for sending control messages (e.g., packet forwarding and route discovery), known as control phase time (CPT). Given a maximum transmission range for narrowband (NB) and wideband (WB) communication, the knowledge of link duration between communicating radios are of major concern, particularly for low latency and reliable communication requirements. Many existing techniques focus on topology control by exchanging mobility parameters in control transmissions, increasing the delay in data transmission. Therefore, it is a non-trivial task to calculate the expected time for the control transmissions due to the confrontation of speed and random movement of nodes. This paper presents a novel methodology to estimate a suitable time to execute the control phase based on the lifetime of communication links between SDRs in tactical MANETs. It uses stochastic distribution to make a network capable of effectively figuring out operative connectivity. The proposed methodology evaluates the maximum network connectivity based on the distances between communicating radios and radio transmission ranges for different quality-of-service (QoS) requirements. The simulation results validate that the proposed methodology’s CPT estimations are more appropriate for the timely link-formations in tactical radio MANETs. The proposed technique is generic and can be applied to any MANET environment using different mobility models.

The paper presents a novel approach for tactical networks in which radios form self-regulatory virtual sub-nets on the basis of control messages. The design uses hybrid of frequency division multiple access (FDMA) and time division multiple access (TDMA) approaches with less call setup delays and increases in network throughput. This multiple sub-nets based distributed algorithm exploits available spectrum resources, provides collision-free simultaneous transmissions and autonomous selection of time slots over different frequency sub-bands with self-organizing and self-forming network capabilities. We propose two schemes which provide multi-channel medium access control with autonomous scheduling of time slots for software-defined radios (SDRs). The schemes use request and acknowledgment frames as control messages to devise a distributed solution. These control messages alone help to form virtual sub-nets, preserve frequency channels and schedule transmissions over multiple time slot intervals, depend on the type of data messages. In order to demonstrate the behavior of sub-nets for tactical networks, theoretical findings are exploited with experimental analysis to provide practical implementation of schemes using contention-free time slotted common control channel for nodes coordination and time slotted collision-free multi-channel environment for data transmissions. The results show the effective coexistence of multiple transmissions in a network with increase in network throughput and lower the data latency up-to 76.8%, when compare to conventional MAC protocols. The design is workable for time sensitive, mission critical networks and expected to support multi-hop transmissions in similar manner.

Unlike conventional mobile ad hoc networks, tactical networks, which provide communication of software-defined radios (SDRs) in mission critical and time-sensitive applications, require cognitive functions across the TCP/IP stack to encounter strict constraints while providing smooth incorporation with IP-based applications. The tactical applications are mission-critical and thus pose unique requirements for the network, including decentralized control and mission specific latency bounds for end-to-end data delivery. This paper presents a mathematical model for a cross-layer design, which optimizes trade-offs among different configurations of the SDRs to achieve maximum performance in terms of energy efficiency, reliable packet delivery at an appropriate data rate and within affordable latency bounds in multi-hop tactical networks. The proposed model is used in a number of mission-critical network scenarios to demonstrate enhanced performance, where SDRs effectively adapt to the dynamic environment.

This paper presents a framework for public healthcare data acquisition and management model based on standard protocol for its easy adoption by any country or international health organizations. The model assumes basic digitization of electronic health record (EHR) at basic health facilities. Thus far, the models in the literature have utilized EHR in multiple secondary contexts; however, there is a gap in developing an integrated and comprehensive framework that addresses the use of EHR in a standardized way for public health, privacy issue by anonymizing patient specific information, fusing multiple records with slight changes in the same information, augmenting a broad spectrum of contextual data, and so on. We present a framework that can be used in the context for acquisition and transmission of EHR from multiple sources as an evidence base for addressing public health-related activities, including surveillance, registries, and immunization record keeping while addressing all the gaps we have identified in the literature that are critical for developing countries. In addition, EHR data are also effectively processed to serve as a knowledge base for building artificial intelligence-based research models. We, in our model, utilize Health Level Seven (HL7) as an interoperability health standard and recommend creation of specialized data marts to support public health and research-related knowledge bases. The proposed framework in its adoption provides a very effective platform for generating alerts and alarms along with providing statistics for better planning of healthcare-related issues at national, district, or at any level of administrative hierarchy. It is applicable to any country even when there is no standard EHR and has hospitals working in silos with limited digitalization. We have validated this framework for its mapping to a national level public health hierarchy in Pakistan.

Abstract Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.

A major fraction of multimedia stream contents tends to be redundant and leads to wastage of storage capacity and channel bandwidth. In order to eliminate surplus data, standard video compression algorithms exploit spatial and temporal correlation present in video sequence. However, in case of a multisensor network, intersensor statistical redundancy is the most significant factor in acquiring efficient link utilization as well as making resultant findings valuable to the end user. In this paper, an extension to our previously proposed scheme has been presented to accomplish performance goals of a multisensor environment. Standard MPEG codec has been used to accomplish distributed motion compensation in prespecified directions known as directional correlation. Video frame correlation has been estimated locally at the camera node as well as across different nodes, defined as node communication strategies. Further, receiver feedback assists in quality control after reconstitution by decoder assessment. Results estimated have been analyzed for saving ratios and multimedia quality. Results analysis illustrates increased gains in frame quality and compression saving, achieved through reducing node displacement from the reference node N R .

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network.

Abstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.