This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution.

This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain the shortest paths for the nodes to balance the flowing traffic within the network and increase its lifetime. However, the optimal link cost values and the maximum achievable lifetime are not known for the majority of the existing schemes. A framework is provided in this work to analytically derive the best achievable performance that can be obtained by any distributed routing algorithm based on the shortest-path approach. Given a network configuration and an energy consumption model, the presented framework provides the optimal link cost assignment which yields the maximum lifetime in a distributed shortest-path routing strategy. The results are extended to the case of variable link cost assignment as well. A heuristic algorithm is also developed to obtain approximate solutions to the best performance problem with limited computational complexity. In particular, the proposed framework provides the optimal route selection as a benchmark to evaluate the energy efficiency of existing routing algorithms.

This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a given function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a prescribed weight function. According to these strategies, each sensor detects coverage holes within its Voronoi polygon, and then moves in a proper direction to reduce them. Since the coverage priority of different points in the field is not the same, the target location of each sensor is determined based on the weights of the points inside the corresponding Voronoi polygon (or its vertices). Simulations confirm the theoretical results.