Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire network, are used to characterize social relations in Mobile social Networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these Networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

Wireless rechargeable Sensor Networks (WRSNs) are widely used in many fields. However, the limited battery capacity of Sensor nodes (SNs) prevents its development. To extend the battery life of SNs, they can be charged by a Mobile charger (MC) equipped with radio frequency-based wireless power transfer (WPT). The paper addressed the issue of optimizing route planning and charging based on an MC with directional charging in on-demand Networks. A mixed integer linear programming model (MILP) is proposed to obtain the appropriate stopping points (SPs) and orientation charging angles to respond to input requests in the shortest possible time and with minimum energy consumption. First, to select the SPs and the orientation charging direction, we utilize a clustering and discretization technique while minimizing the number of SPs and maximizing the charging cover. Then, to decrease the charging time of the required SNs as well as the MC's energy consumption, we propose a heuristic search algorithm for adjusting the moving path for the directional Mobile charger. Finally, experimental simulations are performed to evaluate the performance of the proposed directional charging scheduling algorithm, and the results reveal that the suggested approach outperforms existing studies in terms of MC energy consumption, charging delay, and distance traveled.

In intrusion detection applications, wireless Sensor Networks are commonly used. Many research literature papers are aimed at generating and evaluating the information on intruder detection in terms of probability of detection and false alarm rates. In two modalities, the model for acoustic signal and the Sensor probability model, and in this research paper, the problems of passive motive intrusion detections have been solved. The aim is to establish a three-stage hierarchy to determine if Mobile intruders are present. The Sensor nodes at the fundamental level have a k-mean clustering grouping. For binary hypothesis testing, the strengths or probabilities in the cluster head are employed. Cluster leaders send their judgments to the Fusion Centre (FC) after completing a Likelihood Ratio Test (LRT) to ensure invaders are correctly inferred. A numerical analysis of the signals received determines the optimal value for probability computation. The resulting fusion rule maximizes detection likelihood regarding the allowed falsifying rates. The number of absolute Sensor nodes determines the exact fusion rule. Compared to earlier fusion rules, simulation results show that the new fusion rule has a better ability to follow Mobile invaders and enhanced accuracy and detection speed.

One of the fundamental problems in wireless Sensor Networks is to provide area coverage for a specific task. This paper addresses the problem of default unknown network coverage by a group of Sensor nodes with nonidentical coverage and communication capabilities. The Sensors are Mobile and detect the area in a distributed manner and provide optimal coverage. First, we model the coverage optimization problem as a repetitive multiplayer game in which a utility function is formulated to consider the quality of the coverage. Then, we propose a distributed payoff-based learning algorithm in which each Sensor tries to maximize its utility function by moving to uncovered locations. The simulation results show the performance of the proposed algorithm.

One of the fundamental problems in wireless Sensor Networks is to provide area coverage for a specific task. This paper addresses the problem of default unknown network coverage by a group of Sensor nodes with nonidentical coverage and communication capabilities. The Sensors are Mobile and detect the area in a distributed manner and provide optimal coverage. First, we model the coverage optimization problem as a repetitive multiplayer game in which a utility function is formulated to consider the quality of the coverage. Then, we propose a distributed payoff-based learning algorithm in which each Sensor tries to maximize its utility function by moving to uncovered locations. The simulation results show the performance of the proposed algorithm.

Network connectivity is one of the major design issues in the context of Mobile Sensor Networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in Mobile Sensor Networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul Networks. In the proposed scheme, the Sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.

In this paper, the functionality of Mobile wireless Sensor Networks (MWSNs) is analytically modeled and evaluated using stochastic reward nets (SRNs). In MWSNs, Mobile nodes can move around to collect data from the environment and send it to the sink. These nodes use a limited battery as the power source that can be charged according to environmental conditions. If the battery does not have enough power, the Mobile node is disabled, and therefore it cannot move around and collect/send data. The data collected by the nodes will expire if they are not received by the sink in a timely manner. In order to avoid data expiration, Mobile nodes can send their data to other nodes around themselves, but this also increases the power consumption because of further communication. Therefore, environmental and movement conditions, and communication between nodes can have a major impact on the functionality of the MWSNs. These challenges are considered in the paper and the proposed models analyze the impact of different environmental, communication, and movement conditions on the functionality of MWSNs. The results obtained from the proposed models demonstrate that the environmental and movement conditions have a greater impact on system functionality than the communication conditions.

The Delay Tolerant Mobile Sensor Networks (DT(MSNs)) distinguish themselves from conventional Sensor Networks by means of several features such as node mobility, loose connectivity, and delay tolerability. In comparison with conventional Networks, prominent feature of these Networks is that there is not end-to-end path between source and destination. Due to energy limitation, one of the crucial issues in large-scale Networks is energy storage that can be overcome this problem by distribution of multi-sink in a network at the same time. In this paper, in order to covering large area, we determine a new radio range to sink nodes that prevents the presence of nodes in the radio range of multiple sinks and sending data to a large number of sinks. Then, we represent Replica Adaptive Data Gathering Protocol. Simulation results indicate that the proposed protocol significantly improves message delivery rate with lower transmission overhead and also lower delay in data delivery than the other existing DT(MSNs) routing protocols.