Wireless sensor networks (WSNs) have gained world-wide attention in recent years. Recent advances in wireless communications and electronics have enabled the development of low-cost, short in communication range, limited-power and multi-functional sensor nodes. WSNs can be defined as a self-configured and infrastructure-less wireless networks, which monitors physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and cooperatively pass their data through the network to a main location or sink where the data can be observed and analysed. A sink or base station acts like an interface between users and the network. Typically a WSN contains hundreds to thousands of sensor nodes. The sensor nodes can communicate among themselves using radio signals. A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components.
Sensor nodes monitor the collected data to transmit along to other sensor nodes by hopping. During the process of transmission, monitored data may be handled by multiple nodes to get to gateway node after multihop routing, and finally reach the management node through the internet or satellite. The user can configures and manages the WSN with the manage node, publish monitoring missions and collection of the monitored data.
The sensor nodes are deployed in large numbers provide unprecedented opportunities for a huge of applications of the WSNs in many different fields such as military target tracking and surveillance, natural disaster relief, biomedical health monitoring, hazardous environment exploration and seismic sensing. The foundation and key of applications of WSNs is the location information of sensor nodes. If nodes can not locate their position, or they are located with inaccurate position, the data which is collected by nodes is not useful for applications of WSNSs. The positioning accuaracy is proved that it is very sensitive to the availability of line-of-sight (LOS), which implies that non-line-of-sight (NLOS) environments can make a considerable error in node location estimate. In addition, energy consumption of wireless sensor nodes is also extremely important problem, because the energy of nodes is difficult or cannot be supplied after deployment.
To reduce the effects of the NLOS environments on the positioning accuracy and saving the energy of the nodes consumed in the positioning process, we propose the following solutions.
1. Aim at the problem of power-limited of nodes in the WSN: we propose a constrained optimization algorithm that allocates the transmission power of the anchors while remaining the positioning accuracy satisfies a pre-specified metric, which is defined by Bayesian Fisher Information Matrix (B-FIM). In the scenarios where a high accuracy in target’s location is required, positioning accuracy and energy consumption these two conflict metrics should be balanced, meaning that they should be improved at the same time. Therefore, the Non-domined Sorting Genetic Algorithm (NSGA-II) is used to solve this dual problem. We model the energy consumption of nodes in the network by using the Carrier-Sense Multiple-Access/Collision-Avoidance (CSMA/CA) technique based IEEE 802.11 protocol in combination with Request-To-Send (RTS)/Clear-To-Send (CTS) mechanism to perform the communication between nodes. Otherwise, based on the mathematical model of Direction-of-Arrival (DOA) estimation error variance as a function of Received-Signal-Strength (RSS) we derive the Cramer-Rao Bound (CRB) of achievable accuracy of the target’s estimate position as the objective function of positioning error. The simulation results show that: (1) the B-FIM constrained based optimization localization alogirhtm can significantly reduce the energy consumption of nodes in compared to scheme of uniformly power allocation; and (2) the NGSA-II based localization algorithm can effectively solves the tradeoff between localization accuracy and energy consumption.
2. Aim at the problem of power-limited of nodes in the WSN: we propose an energy-efficient localization game, which can allocate the transmission power and the sleep times of the anchor nodes simultaneously. As we knew, game theory provides the ideal framework to design the efficient and robust distributed algorithms. It is a natural tool for modeling and addressing our problem. We study the energy saving problem of nodes for the hybrid DOA/RSS localization scheme in WSNs. To solve this problem, both the transmission power and the sleep times of the anchor nodes are allocated while the target is required to be localized with a pre-specified accuracy. The leader-follower Stackelberg game is used to model our problem, in which the target is the follower and the set of the target’s neighbor anchor nodes, named CANs is the leader. In this game, the CANs plays as the leader who take actions first, and the target plays as the follower then takes actions accordingly. We prove that there exists a unique Stackelberg Equilibrim (SE) in this game which is different to the Nash Equilirium (NE) in the standard game. A NE is the status where no individual player can do strictly better by deviating, holding the actions of other players. But the SE is the optimal strategy of the leader, given the best response action of the follower to the leader’s strategy, together with the optimal strategy of the follower corresponding to the optimal strategy of the leader.
We divide the game into two hierarchical levels: the target plays a buyer-level game and the anchors play a seller-level game. Each player is selfish and wants to maximize its benefit. The target can be seen as a buyer who buys the transmission power from the anchors to maximize its benefit at the least possible cost. CANs can be viewed as a seller who has knowledge of target’s intelligence and gets the sleeping time as the payment. We prove the existence and uniqueness of the SE by giving closed-form expressions for the SE strategies of both the target and the CANs. The superior performance of proposed algorithm is valided through simulation results.
3. Aim at the problem of inaccuracy positioning in NLOS conditions, a non-parametric belief propagation (NBP)-based localization algorithm in the NLOS environments for WSNs is proposed. According to the amount of prior information known about the probability of a range measurement is in NLOS environment and distribution parameters of the NLOS error distributions, three different cases of the maximum a posterior (MAP) localization problems are introduced. Firstly is the idealized case where an understanding of NLOS condition measurements and distribution parameters of the NLOS errors is perfectly known. The second case deals with the scenario where we do not know which ranges are in the NLOS condition, however, we know the probability that a link of a pair of nodes is in NLOS condition and the corresponding distribution parameters of the NLOS errors. The worst case is the third one because only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing (ML-SA)-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization alogorithm for every case. Therefore, in the NLOS environments, the localization algorithm can obtain the location estimate with high accuracy by using the NBP method.