Propagation models are used to abstract the actual propagation characteristics of electromagnetic waves utilized for conveying information in a compact form (i.e., a model with a small number of parameters). The correct modeling of propagation and path loss is of paramount importance in wireless sensor network (WSN) system design and analysis [1]. Most of the important performance metrics commonly employed for WSNs, such as energy dissipation, route optimization, reliability, and connectivity, are affected by the utilized propagation model. However, in many studies on WSNs, overly simplistic and unrealistic propagation models are used. One of the reasons for the utilization of such impractical propagation models is the lack of awareness of experimentally available WSN-specific propagation and path-loss models. In this article, necessary succint background information is given on general wireless propagation modeling, and salient WSN-specific constraints on path-loss modeling are summarized. Building upon the provided background, an overview of the experimentally verified propagation models for WSNs is presented, and quantitative comparisons of propagation models employed in WSN research under various scenarios and frequency bands are provided.

Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors (i.e., packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.

Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) with a potential to create opportunities for enhancing the capacity of the network by dynamically moving the supply towards the demand while facilitating the services that cannot be provided via other means efficiently. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAV-BS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAV-BS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAV-BS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

Connectivity maintenance is an important requirement in wireless sensor and actuator networks (WSANs) because node failures can, potentially, lead to destructive changes in the network topology, which, in turn, can create a partitioned network. Preserving $k$ -connectivity in a WSAN is important for keeping stable connections. A $k$ -connected network is a network that remains connected after removing any $k$ -1 nodes. Higher $k$ values provide more reliable connectivity and a higher level of fault tolerance. In this article, we present a distributed $k$ -connectivity restoration approach for heterogeneous WSANs where the nodes can be static or mobile. In the proposed algorithm, each node identifies the mobile nodes in the network and its 2-hop local subgraph. After a node is incapacitated, a neighbor of the failed node calls a mobile node with minimum moving cost to the location of the failed node if the failure reduces $k$ . A minimum cost movement path between a neighbor of the failed node and a mobile node is constructed by considering the locations of the nodes, moving costs, and obstacles. Testbed experiments and comprehensive simulations reveal that the proposed distributed algorithm is capable of restoring $k$ -connectivity with up to 35.5% lower sent Bytes and up to 40.9% lower movement cost than the existing algorithms.