Optimal Sampling Rate Assignment with Dynamic Route Selection for Real-Time Wireless Sensor Networks

With the decrease in the cost and in the size of computing devices, wireless sensor networks (WSNET) have the potential of being composed by an extremely large number of nodes offering multiple services. Such networks have the capability of executing multiple tasks concurrently by allocating simply a fraction of their resources. Alternatively, many smaller wireless networks may collaborate to execute a larger, unforeseen application. In both cases a routing scheme other than the prevailing one may improve the efficiency of the task or the application being executed, reducing the energy consumption in the network. We posit that to fully tap into the potential of such networks a new routing infrastructure is needed, one that allows switching between different routing schemes dynamically as required by the applications being deployed, the conditions of the network as a whole and the existing locality information. We show in this paper how dynamic routing scheme selection can be achieved when sensor networks are overlaid with a virtual attribute based cluster hierarchy. We present analytical results for our scheme and show the expected improvement that can be achieved.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

The development of an infrastructure for multiple autonomous agents, with an application to urban traffic signal control, is described. The agent-based infrastructure, Cybele, allows for distributed computing, interagent communication, agent migration, and computational resource allocation. The agents that are used to solve the traffic signal control problem are known collectively as DAARTS (Decentralized Adaptive Agents for contRol of Traffic Signals). DAARTS adopts a hierarchical multiagent-based architecture in which the lowest level (intersection agents) involves individual intersection-traffic dynamics and phase selection based on “local” information, while higher levels take into account the supervisory (network-level) dynamics. The controller design is based on a receding-horizon model predictive control approach. Coordination between intersections is achieved in a decentralized manner at the lowest level. The agents are integrated into a simulation test bed with the microsimulator CORSIM, using the DAARTS simulation tool kit. This kit enables communications between the CORSIM real-time extension, the communications management functions of Cybele, and the traffic control agents. The control process is truly distributed, and each of these components can reside on a different computer. Descriptions of all of the software components are given, and the control algorithm is discussed in detail. Some encouraging results from the simulation of a small network are included. Ongoing and future research activities are discussed.

This paper proposes an offloading strategy for communication resources and computing resources. Based on the communication background of NOMA, this strategy combines D2D communication and cellular mobile communication to consider the use of resources in the communication process. According to the performance of communication resources, the power of cellular network users and D2D users is controlled to make the task unloading decision between users and small MEC server base stations. The purpose is to find the minimum sum of the total cost of the user group within the coverage of the entire base station. The cost is obtained from the task completion time and the base station usage payment. The experimental results show that this method can find the optimal solution of the user group cost, and it also has a good performance in user fairness. In reality, taking D2D communication into consideration for coordinated distribution in a cellular network can greatly reduce the cost of the two.

Wireless Sensor Network (WSN) clustering is useful for developing routing algorithms that improves the scalability and longevity of the network. Cluster Head (CH) is essential for data transmission in the clustered WSN paradigm. Thus several cluster-based routing protocols have been introduced recently. As such, energy constraints affect both the duration of the network and energy-efficient routing. In this paper, Adaptive Grasshopper Optimization Algorithm (AGOA) is introduced for cluster-based routing and CH selection which increases routing efficiency, and maximizes network lifetime. Furthermore, high convergence rate and lower parameter complexity as compared to other optimization techniques. Here, Residual Energy (RE), distance to the Base Station (BS), distance to neighbors, node degree, and node centrality has been used for choosing the best CH. Additionally, the path between the BS and CH can be determined using AGOA. Lastly, the results routing protocols are evaluated using metrics including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average energy consumption, and Network Lifetime.

Wireless sensor networks (WSNs) are becoming increasingly prevalent in numerous fields. Industrial applications and natural-disaster-detection systems need fast and reliable data transmission, and in several cases, they need to be able to cope with changing traffic conditions. Thus, time-slotted channel hopping (TSCH) offers high reliability and efficient power management at the medium access control (MAC) level; TSCH considers two dimensions, time and frequency when allocating communication resources. However, the scheduler, which decides where in time and frequency these communication resources are allotted, is not part of the standard. Orchestra has been proposed as a scheduler which allocates the communication resources based on information collected through the IPv6 routing protocol for low-power and lossy networks (RPL). Orchestra is a very elegant solution, but does not adapt to high traffic. This research aims to build an Orchestra-based scheduler for applications with unpredictable traffic bursts. The implemented scheduler allocates resources based on traffic congestion measured for the children of the root and RPL subtree size of the same nodes. The performance analysis of the proposed scheduler shows lower latency and higher packet delivery ratio (PDR) compared to Orchestra during bursts, with negligible impact outside them.

—Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way, since their energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor networks. In this paper, we introduce a clustering protocol with mode selection (CPMS) for wireless sensor networks. Our scheme improves the performance of BCDCP (Base Station Controlled Dynamic Clustering Protocol) and BIDRP (Base Station Initiated Dynamic Routing Protocol) routing protocol. In CPMS, the base station constructs clusters and makes the head node with the highest residual energy send data to the base station. Furthermore, we can save the energy of head nodes by using the modes selection method. The simulation results show that CPMS achieves longer lifetime and more data message transmissions than current important clustering protocols in wireless sensor networks.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

Wireless Sensor Networks (WSNs), comprising of tiny nodes with limited energy, computational and communication resources, are being widely used in various application areas ranging from pollution and weather monitoring to military. Even though every application may not require data to be exchanged in a secure manner, few WSN applications may have certain security requirements. The data is encrypted by a sender and sent over the wireless links and the same is decrypted at the receiver. To meet this purpose, symmetric key cryptographic (SKC) algorithms may be used. Such SKC primitives require keys to be made available before carrying out the data transfer between the nodes. Alternately, Public Key Cryptographic techniques, such as RSA algorithm can be considered. Even though, RSA is a popular algorithm providing good security level, it is computationally intensive involving large key sizes. The RSA can not be used in WSNs, as the nodes have limited resources. Presently, it is infeasible to implement the RSA algorithm using any of the WSN nodes commercially available. Elliptic curve cryptography (ECC), another public key cryptographic (PKC) algorithm providing same level of security with smaller key size requirements, can be used as an alternative in order to provide security in WSN applications. ECC encryption and decryption use domain parameters, which includes the Generator point to be published. In most of the outdoor WSN applications, the deployment of the nodes is random and the nodes could be captured and an attacker could launch man-in-middle (MIM) attack, and break the public key thereby leading to security breach in the network. A technique to overcome such an attack is proposed in this work and the same is compared with two other similar approaches.

The dynamic routing algorithm for renewable wireless sensor networks with wireless power transfer

Wireless sensor networks (WSNs) abiding a huge quantity of inexpensive, low consumption and intense sensor nodes and sole or additional decline or base stations. Those nodes are tiny in dimension and can carry out numerous significant affairs, as well as event sensing, in order processing, and data communication. Owed to different compensation such as simplicity of consumption, comprehensive transmission range, and identity association, WSNs have been interchanging the conventional networks. The promising and innovative network of wirelessly connected devices, which consist of video, audio streams, scalar sensor data and others, is Wireless Multimedia Sensor Network (WMSN). The multimedia data depends on superiority to overhaul (Qos) parameters, like await, channel utilization, network life time throughput and distortion etc is the main aim to optimized delivery of these networks. In multimedia network each packets are communicated with each other so not on time incoming packets and lots of packets are evenly. In WMSNs challenging task to guarantee resource constrained, high data rate, and soft delay limit beside with energy reduced. In this paper, we provide Survey on comparison of different protocol of Wireless sensor network and wirelesss multimedia sensor network like Greedy Perimeter Stateless Routing for Wireless Networks (GPSR), Real-time and Energy Aware Qos routing protocol (REAR), Temporary ordered routing Algorithm (TORA) and Dynamic source routing (DSR). This different protocols with WMSN are compared in requisites of throughput, end-to-end delay, network life time.

Exclusive benefits gained by using the image and video sensors in Wireless Sensor Networks (WSNs), make the Wireless Video Sensor Networks (WVSNs), design and development of their structures, a trending topic. The main challenges of WSNs which are more critical in WVSNs are throughput and end-to-end delay. The radio transceivers recently used in WSN motes can work on different channels with very low switching time. Therefore, many multichannel protocols are proposed to improve the efficiency and throughput and decrease the packet drop rate of the network. Proposed algorithms rarely provide a particular solution to the end-to-end problem. Such delays are necessary parameters in real-time applications. In this paper a real-time multichannel protocol called RTMCH is suggested to address the end-to-end delay of a stream in WVSN. A flow-based channel assignment strategy is used for this purpose. Orthogonal channels are assigned to each flows based on many-to-one data-flow to minimize the contention and collision between different flows. Transmission power is a parameter that can be controlled to achieve desired link quality and to control link delay. Channel assignment problem aligns to a constrained optimization problem to make the specified end-to-end delay of each flow. A channel assignment and real-time packet forwarding scheme are then presented. Simulation results based on realistic channel and radio model shows that the RTMCH can efficiently use multiple channels and transmission power to meet specified end-to-end delay. The results also show better performance for RTMCH over a recent real-time protocol and basic multichannel schemes.

The conventional setup of a wireless sensor network is composed of several sensor nodes and one or more sinks. The network topology and data collection techniques are then optimized towards efficient collection of the sensed data at the sink(s). In this paper, we present a novel network coding technique based on Compressed Sensing that allows each node to operate as a sink. Our network coding technique efficiently disseminates a number of linear combinations of the sensed data. After the dissemination phase, the entire sensed data is available by querying any node of the network. This is especially useful for distributed control using a wireless sensor and actuator network or in scenarios where the end user needs to access the global state of the environment from any node in its vicinity; e.g., when the end user is mobile.

Data aggregation has been widely recognized as an efficient method to reduce energy consumption in wireless sensor networks, which can support a wide range of applications such as monitoring temperature, humidity, level, speed etc. The data sampled by the same kind of sensors have much redundancy since the sensor nodes are usually quite dense in wireless sensor networks. To make data aggregation more efficient, the packets with the same attribute, defined as the identifier of different data sampled by different sensors such as temperature sensors, humidity sensors, etc., should be gathered together. However, to the best of our knowledge, present data aggregation mechanisms did not take packet attribute into consideration. In this paper, we take the lead in introducing packet attribute into data aggregation and propose an Attribute-aware Data Aggregation mechanism using Dynamic Routing (ADADR) which can make packets with the same attribute convergent as much as possible and therefore improve the efficiency of data aggregation. This goal cannot be achieved by present static routing schemes employed in most of data aggregation mechanisms since they construct routes before transmitting the sampled data and thus can not dynamically forward packets in response to the variation of packets at intermediate nodes. Hence, we present a potential-based dynamic routing scheme which employs the concept of potential in physics and pheromone in ant colony to achieve our goal. The results of simulations in series of scenarios show that ADADR indeed conserve energy by reducing the average number of transmissions each packet needs to reach the sink and is scalable with regard to the network size.

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air-ground integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields such as smart homes, smart factories and smart cities to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation-intensive, latency-sensitive, and energy-consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this paper, we study mobile edge computing (MEC) for AI applications in air-ground integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation and communication resource constraints. Considering the formulated problem is non-convex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.