Intelligent energy efficient localization using variable range beacons in industrial wireless sensor networks

In many applications of industrial wireless sensor networks, sensor nodes need to determine their own geographic position coordinates so that the collected data can be ascribed to the location from where it was gathered. We propose a novel intelligent localization algorithm which uses variable range beacon signals generated by varying the transmission power of beacon nodes. The algorithm does not use any additional hardware resources for ranging and estimates position using only radio connectivity by passively listening to the beacon signals. The algorithm is distributed, so each sensor node determines its own position and communication overhead is avoided. As the beacon nodes do not always transmit at maximum power and no transmission power is used by unknown sensor nodes for localization, the proposed algorithm is energy efficient. It also provides control over localization granularity. Simulation results show that the algorithm provides good accuracy under varying radio conditions

Energy-efficient location estimation using variable range beacons in wireless sensor networks

In a randomly deployed wireless sensor network, sensor nodes must determine their own geographic position coordinates so that the collected data can be ascribed to the location from where it was gathered. We propose a localization algorithm which uses variable range beacon signals generated by varying the transmission power of beacon nodes. The algorithm does not use any additional hardware resources for ranging and estimates position using only radio connectivity by passively listening to the beacon signals. The algorithm is distributed, so each sensor node determines its own position and communication overhead is avoided. As the beacon nodes do not always transmit at maximum power and no transmission power is used by unknown sensor nodes for localization, the algorithm is also energy efficient. It also provides control over localization granularity. Simulation results show that the algorithm provides good accuracy under varying radio conditions

A distance vector hop-based secure and robust localization algorithm for wireless sensor networks

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes

Energy Efficiency and Throughput Optimization in 5G Heterogeneous Networks

Device to device communication offers an optimistic technology for 5G network which aims to enhance data rate, reduce latency and cost, improve energy efficiency as well as provide other desired features. 5G heterogeneous network (5GHN) with a decoupled association strategy of downlink (DL) and uplink (UL) is an optimistic solution for challenges which are faced in 4G heterogeneous network (4GHN). Research work presented in this paper evaluates performance of 4GHN along with DL and UL coupled (DU-CP) access scheme in comparison with 5GHN with UL and DL decoupled (DU-DCP) access scheme in terms of energy efficiency and network throughput in 4-tier heterogeneous networks. Energy and throughput are optimized for both scenarios i.e. DU-CP and DU-DCP and the results are compared. Detailed performance analysis of DU-CP and DU-DCP access schemes has been done with the help of comparisons of results achieved by implementing genetic algorithm (GA) and particle swarm optimization (PSO). Both these algorithms are suited for the non linear problem under investigation where the search space is large. Simulation results have shown that the DU-DCP access scheme gives better performance as compared to DU-CP scheme in a 4-tier heterogeneous network in terms of network throughput and energy efficiency. PSO achieves an energy efficiency of 12 Mbits/joule for DU-CP and 42 Mbits/joule for DU-DCP, whereas GA yields an energy efficiency of 28 Mbits/joule for DU-CP and 55 Mbits/joule for DU-DCP. Performance of the proposed method is compared with that of three other schemes. The results show that the DU-DCP scheme using GA outperforms the compared methods

Optimal learning paradigm and clustering for effective radio resource management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the $\epsilon -$ greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning

Interference Avoidance in Cognitive Radio Networks Using Cooperative Localization

In the recent years, there has been a tremendous increase in the use of wireless medium and radio frequency spectrum due to the development of new types of wireless networks, applications, and enabling technologies. Consequently, the radio frequency spectrum is getting overcrowded due to this increasing demand. Traditionally, frequency bands are allocated to licensed users for their specific use. Cognitive radio allows secondary users to communicate using these frequency bands. However, this may result in interference to the primary users. Information of the relative positions of the primary and secondary users and the distance between them can be exploited to avoid this interference. In our work, we use cooperative localization strategy to determine the distance between the secondary and primary users. This distance information is then utilized to adjust the transmission power of the secondary nodes so that the interference threshold of the primary users is not exceeded. The proposed methodology is evaluated using simulation experiments. Different aspects of the proposed algorithm including location and distance estimation, channel availability, and channel capacity against transmission power and path loss are evaluated. The results show that the proposed scheme is able to achieve considerable gains as a consequence of interference avoidance