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This paper aims to analyze the security issues that arise when integrating wireless sensor networks (WSN) and the internet. Also, it seeks to review whether existing technology mechanisms are suitable and can be applied in this context.

The paper considers the possible approaches that can be used to connect a WSN with the internet, and analyzes the security of their interactions.

By providing the services of the network through a front‐end proxy, a sensor network and the internet can interact securely. There are other challenges to be solved if the sensor nodes are integrated into the internet infrastructure, although there exists interesting advances on his matter.

The complete integration of sensor networks and the internet still remains as an open issue.

With the current state of the art, it is possible to develop a secure sensor network that can provide its services to internet hosts with certain security properties.

The paper studies the interactions between sensor networks and the internet from the point of view of security. It identifies both solutions and research challenges.

The purpose of this paper is to review recent developments in wireless‐sensing technologies, products and applications.

The paper describes wireless sensor technologies, discusses the research efforts aimed at yielding battery‐free devices and considers a range of products, developments and applications.

The paper shows that emerging families of wireless sensors are being used in a wide range of applications in many industries. These include process control, condition and structural monitoring, energy management, automotive safety, healthcare and security. Major efforts are underway to develop battery‐free devices based on energy‐harvesting and other techniques.

The paper provides a technical review of the rapidly developing field of wireless‐sensing technology.

The purpose of this paper is to search an energy balance routing in the wireless sensor networks (WSN) and lengthen the life of the networks.

To save energy in the WSN, some routing protocols search routing with the minimum total energy consumption of the network, and others reduce data redundancy by data aggregation. But if the distribution of energy consumption was not even, the energy of some nodes would be exhausted rapidly and thus the whole network would break down. Thus, an energy balance routing notion, including communication energy cost of the routing, remaining energy of communication sensors and sensor load have been involved. Then a new algorithm, mouse colony optimization and simulated annealing (SA), is advanced to solve the problem of energy balance routing in the network.

The energy balance routing, based on mouse colony optimization and SA, performs well and yields better performance than other congener algorithms.

The appointed times of the algorithm is the main limitation which increase the complexity of the algorithm.

A very useful routing in wireless sensor networks.

The new approach of energy balance routing notion, including communication energy cost of the routing, remaining energy of communication sensors and sensor load. The new algorithm, mouse colony optimization algorithm, simulated mice action, was proposed to solve the energy balance routing of the network.

Sensing coverage is a critical issue in sensor network deployments. The paper aims to propose a novel scheme to maintain the sensing coverage in sensor networks, which is termed coverage‐aware self‐scheduling (CASS).

The paper describes a generic unifying framework to incorporate different connectivity and coverage maintenance schemes. Simulations are carried out under the framework by integrating CASS with an existing connectivity maintenance scheme ‐ the low‐energy adaptive clustering hierarchy.

Different from the existing work on coverage maintenance, CASS probabilistically schedules sensing activities according to the sensor's contribution to the sensing coverage of the whole sensor network. CASS reduces the number of active sensors to maintain certain coverage. Besides the sensing coverage, the connectivity of the network topologies is required for the purpose of communicating among sensors to collect sensing data. Simulation results show that CASS can considerably improve the energy efficiency of sensing coverage with low communication and computation overhead.

The paper shows that CASS is designed to allow sensors with higher coverage contribution to have more chance to sense.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.

Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Load balancing is an effective enhancement to the proposed routing protocol, and the basic idea is to share traffic load among cluster members to reduce the dropping probability due to queue overflow at some nodes. This paper aims to propose a novel hierarchical approach called distributed energy efficient adaptive clustering protocol (DEACP) with data gathering, load-balancing and self-adaptation for wireless sensor network (WSN). The authors have proposed DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well and the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

A WSN consists of large number of wireless capable sensor devices working collaboratively to achieve a common objective. One or more sinks [or base stations (BS)] which collect data from all sensor devices. These sinks are the interface through which the WSN interacts with the outside world. Challenges in WSN arise in implementation of several services, and there are so many controllable and uncontrollable parameters (Chirihane, 2015) by which the implementation of WSN is affected, e.g. energy conservation. Clustering is an efficient way to reduce energy consumption and extend the life time of the network, by performing data aggregation and fusion to reduce the number of transmitted messages to the BS (Chirihane, 2015). Nodes of the network are organized into the clusters to process and forwarding the information, while lower energy nodes can be used to sense the target, and DEACP makes no assumptions on the size and the density of the network. The number of levels depends on the cluster range and the minimum energy path to the head. The proposed protocol reduces the number of dead nodes and the energy consumption, to extend the network lifetime. The rest of the paper is organized as follows: An overview of related work is given in Section 2. In Section 3, the authors propose an energy efficient level-based clustering routing protocol (DEACP). Simulations and results of experiments are discussed in Section 4. In Section 5, the authors conclude the work presented in this paper and the scope of further extension of this work.

The authors have proposed the DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well, the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

Wireless sensor networks (WSNs) will profoundly influence the ubiquitous computing landscape. Their utility derives not from the computational capabilities of any single sensor node, but from the emergent capabilities of many communicating sensor nodes. Consequently, the details of communication within and across single hop neighborhoods is a fundamental component of most WSN applications. But these details are often complex, and popular embedded languages for WSNs provide only low‐level communication primitives. We propose that the absence of suitable communication abstractions contributes to the difficulty of developing large‐scale WSN applications. To address this issue, we present the design and implementation of a Remote Procedure Call (RPC) abstraction for nesC and TinyOS, the emerging standard for developing WSN applications. We present the key language extensions, operating system services, and automation tools that enable the proposed abstraction. We illustrate these contributions in the context of a representative case study, and analyze the overhead introduced when using our approach. We use these results to draw conclusions regarding the suitably of our work to resource‐constrained sensor nodes.

A decade and a half after the debut of pervasive computing, a large number of prototypes, applications, and interaction interfaces have emerged. However, there is a lack of consensus about the best approaches to create such systems or how to evaluate them. To address these issues, this paper aims to develop a performance evaluation framework for pervasive computing systems.

Based on the authors' experience in the Gator Tech Smart House – an assistive environment for the elderly, they established a reference scenario that was used to guide the analysis of the large number of systems they studied. An extensive survey of the literature was conducted, and through a thorough analysis, the authors derived and arrived at a broad taxonomy that could form a basic framework for evaluating existing and future pervasive computing systems.

A taxonomy of pervasive systems is instrumental to their successful evaluation and assessment. The process of creating such taxonomy is cumbersome, and as pervasive systems evolve with new technological advances, such taxonomy is bound to change by way of refinement or extension. This paper found that a taxonomy for something so broad as pervasive systems is very complex. It overcomes the complexity by focusing the classifications on key aspects of pervasive systems, decided purely empirically and based on the authors own experience in a real‐life, large‐scale pervasive system project.

There are currently no methods or frameworks for comparing, classifying, or evaluating pervasive systems. The paper establishes a taxonomy – a first step toward a larger evaluation methodology. It also provides a wealth of information, derived from a survey of a broad collection of pervasive systems.

Energy consumption is a fundamental concern in wireless multimedia sensor networks, even more than that in traditional wireless sensor networks. In fact, sensors are battery‐constrained devices, while multimedia applications, which require higher transmission rates and more extensive processing capacity, produce large quantities of data. The purpose of this paper is to propose a method which could save multimedia nodes' energy during their sleeping and transmitting phases and effectively bring down energy consumption.

Based on the location and direction angle of nodes, areas of the overlapping sensing region between adjacent nodes could be calculated and a sleeping strategy is carried out to reduce energy consumption of inactive nodes. Moreover, an improved image comparison and block transmission methods by utilizing the sequence similar detection arithmetic theory is brought up as well to further save the node's energy.

With the help of the mobile nodes and by dividing the network into grids, sensing direction of multimedia node as well as the area of the overlapping sensing region could be obtained which provide a basis for the sleeping strategy. Furthermore, energy consumption on transmission is also reduced by only transmitting the unmatched blocks. Simulation results show that this method obtains satisfactory performance on prolonging the networks' lifetime.

This paper is of value in presenting an effective energy‐saving strategy for wireless multimedia sensor networks.