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In this chapter different network operations are described in SGL, which may be useful for security applications. Basic network management mechanisms are expressed, capable of working on their own even if traditional communications and internet includings are damaged. These include network creation from scratch, finding paths between nodes, and creating routing tables (RT) allowing for shortest path communications. Also is shown the use of SGT for analysing distributed networks with social flavour by finding strong and weak components in them and changing their topology in crisis situations. Another example is how to outline different communities in a distributed social network, find their topographical centres and evaluate physical distances between them for predicting possible social conflicts, while doing this repeatedly together with simulation of spatial mobility of individuals in time.

Recent wireless communication and electronics technology has enabled the development of low‐cost, low‐power, and multi‐functional sensor nodes. However, the fact that sensor nodes are severely energy‐constrained has been an issue and many energy‐efficient routing protocols have been proposed to resolve it. Cluster‐based routing protocol is one of them. To achieve longer lifetime, some cluster‐based routing protocols use information on GPS‐based location of each sensor node. However, because of high cost, not all sensor nodes can be GPS‐enabled. The purpose of this paper is to propose a simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks (WSN).

Instead of using location information of each sensor node, this approach utilizes information of remaining energy of each sensor node and changes in the number of cluster head nodes dependent on the number of sensor nodes alive. Performance results are presented and compared with some related protocols.

The simulations described in the paper show that both residual energy of each sensor node and changing cluster head nodes depending on the number of sensor nodes alive are very critical factors to obtain performance enhancement in terms of lifetime and data transmission. Especially, in some special environment, the proposal has better performance than GPS‐enabled protocol.

The paper is of value in proposing a simple dynamic clustering approach to achieve energy efficiency for WSN.

This research can contribute to design and develop wireless sensor network (WSN) routing algorithm based on the comparison data. The paper aims to discuss these issues.

This research is a tree-based routing algorithm in order to reduce routing cost on WSNs.

The approach is more efficient than other cluster-based approach.

Originality is high because there is no other paper about cost effect and energy saving both considering at the same time.

The centralized processes of today’s power trading systems are complex and pose a risk of price tampering and hacking. The decentralized and unmodifiable nature of the blockchain technology that has recently been highlighted offers the potential to improve this power trading process. The purpose of this study is to implement a system to apply the blockchain technology to the problem of power trading.

The authors modeled the power trading problem as the interaction between admin, producer and consumer nodes. And a power trading scenario has been created for this model using a blockchain platform called Multichain which is both fast and highly scalable. To verify this scenario, they implemented a trading system using Savoir, a Python-based JsonRPC module.

Experimental results show that all processes, such as blockchain creation, node connectivity, asset issuance and exchange transactions have been correctly handled according to the scenario.

In this study, the authors have proposed and implemented a power trading method that determines price according to the pure market principle and cannot be manipulated or hacked. It is based on the nature of blockchain technology that is decentralized and cannot be tampered.

Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks.

In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function.

All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster.

The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.

For the solution of equations with sparse matrices, the problem of bandwidth reduction is an important issue. Though graph theoretical algorithms are available, the purpose of this paper is to examine the feasibility of ant systems (AS).

For band optimization an ant colony algorithm based on AS is utilized. In this algorithm a local search procedure is also included to improve the solution.

AS algorithms are found to be suitable for bandwidth optimization.

Application of AS to the bandwidth reduction is the main purpose of this paper, which is successfully performed. The results are compared to those of a graph theoretical bandwidth optimization algorithm.

This paper proposes to identify a new model of the fixed voltage nodes (PV nodes) for medium voltage distribution systems analysis. The model is used within backward/forward (b/f) analysis method applied to solve radial and weakly meshed systems.

The model is based on the compensation currents method for multi‐port systems which has been extensively used, within b/f analysis methods, to take into account the presence of meshes and PV nodes.

Test results prove the approach to be more efficient and precise than previous methodologies and put into evidence the good performance of the proposed model in terms of speed and convergence properties.

The model is developed to be used within b/f methodology.

Utilities are quite interested in such items, since the deregulation electricity market in last years has determined an evolution of the medium voltage distribution networks, due to the presence of distributed generation. So it is necessary to take into account and to model in an opportune way the nodes with distributed generators (PV nodes), also in weakly meshed networks, for the load flow analysis.

The paper presents a new model to treat the fixed voltage nodes (PV nodes) for medium voltage distribution networks analysis.

The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.