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This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.

This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.

From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.

This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.

Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.

This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.

The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.

For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.

The purpose of this paper is to examine and model the in-plant operational efficiency of tow trucks of an automobile manufacturing plant. Even though, tow trucks contribute toward the improvement of operational performance, little case-based evidence prevail in the literature. For this purpose, a case study has been conducted in an Indian automobile manufacturer to address the prevailing issues in material handling (MH).

Initially, this paper focuses on grouping of the sequence parts and finding the shortest path among the groups. To elucidate this, an analytical framework based on the distance and stuffing quantity is proposed. A fuzzy Dijkstra’s algorithm is used to solve the issues in grouping of the sequence parts and shortest path among the groups.

This study addressed the four aspects of MH: move cost, time, distance and material by integrating the function of grouping, finding the shortest path and communication with low cost devices. The result shows that logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the production line managers is key performance indicator.

The paper helps the automobile practitioners on increasing the efficiency of tow truck by systemizing the routes. Logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the line managers is key performance indicator.

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.

Isometric feature mapping (Isomap) is a very popular manifold learning method and is widely used in dimensionality reduction and data visualization. The most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood graph. The classical Isomap (C-Isomap) is very slow, due to the use of Floyd’s algorithm to compute the shortest paths. The purpose of this paper is to speed up Isomap.

Through theoretical analysis, it is found that the neighbourhood graph in Isomap is sparse. In this case, the Dijkstra’s algorithm with Fibonacci heap (Fib-Dij) is faster than Floyd’s algorithm. In this paper, an improved Isomap method based on Fib-Dij is proposed. By using Fib-Dij to replace Floyd’s algorithm, an improved Isomap method is presented in this paper.

Using the S-curve, the Swiss-roll, the Frey face database, the mixed national institute of standards and technology database of handwritten digits and a face image database, the performance of the proposed method is compared with C-Isomap, showing the consistency with C-Isomap and marked improvements in terms of the high speed. Simulations also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N³) to O(N²lgN).

Due to the limitations of the computer, the sizes of the data sets in this paper are all smaller than 3,000. Therefore, researchers are encouraged to test the proposed algorithm on larger data sets.

The new method based on Fib-Dij can greatly improve the speed of Isomap.

The purpose of this paper is to present a new approach for finding a minimum-length trajectory for an autonomous unmanned air vehicle or a long-range missile from a release point with specified release conditions to a destination with specified approach conditions. The trajectory has to avoid obstacles and no-fly zones and must take into account the kinematic constraints of the air vehicle.

A discrete routing model is proposed that represents the airspace by a sophisticated network. The problem is then solved by applying standard shortest-path algorithms.

In contrast to the most widely used grids, the generated networks allow arbitrary flight directions and turn angles, as well as maneuvers of different strengths, thus fully exploiting the flight capabilities of the aircraft. Moreover, the networks are resolution-independent and provide high flexibility by the option to adapt density.

As an application, a concept for in-flight replanning of flight paths to changing destinations is proposed. All computationally intensive tasks are performed in a pre-flight planning prior to the launch of the mission. The in-flight planning is based entirely on precalculated data, which are stored in the onboard computer of the air vehicle. In particular, no path finding algorithms with high or unpredictable running time and uncertain outcome have to be applied during flight.

The paper presents a new network-based algorithm for flight path optimization that overcomes weaknesses of grid-based approaches and allows high-quality solutions. The method can be applied for quick in-flight replanning of flight paths.

The purpose of this paper is to review the development of the German advertising industry starting from 1950 to 2018 with a special focus on the American influence.

The paper uses the oral history methodology. The content is based on 27 semi-structured interviews with current and former experts from the German and American advertising industry. An analysis of secondary sources supports the line of argumentation.

The paper confirms the outstanding role of the American influence on the German advertising industry, owing to new standards of professionalism, to novel versions of terminology and to the introduction of the theory of marketing. However, incompatible management styles, increasing global competition and financial pressure diminished the impact. Likewise, the American interference did not suppress the development of specific German industry characteristics such as a strong entrepreneurial culture or sustainable leadership.

This paper provides an overview of the history of German advertising with a focus on advertising agencies in the period from 1950 to today (2018). Further, this paper assesses the special impact of the American influence on the German advertising industry. Further, subjects of investigation are particularities of the German advertising industry, such as special attributes of agency leaders and their relationship with clients, distinct versions of ownership structures, agency service offerings and, finally, the role of creativity.

This chapter develops a methodology to assist critical facility operators in designing physical protection systems to defend against a single adversary (thief, saboteur, terrorist, etc.) attack. The developed methodology utilizes a multicriteria decision-making approach that balances the competing goals of minimal security system cost and maximum system performance. The methodology utilizes a network-based approach to facility security system design and analysis, which locates physical protection (detection, delay, and response) elements throughout a facility. These elements enable the facility owner to prevent attacks through deterrence and to defeat the adversary if he or she chooses to attack. The developed approach results in the ability for the facility operator to assess relative facility and/or infrastructure safety, and make decisions regarding how to optimally allocate resources for physical protection elements to balance cost and performance. A hypothetical example is discussed which demonstrates the usefulness of the developed methodology.

The production of the built environment, as any other industrial production, is a knowledge‐intensive process. Knowledge resides in many teams/parties who are involved in the creation or production process. This paper seeks to discuss the feasibility of social network analysis as a tool for understanding the process of knowledge creation through communication among team members in the construction industry.

Following a literature review of the characteristics of innovation, knowledge and social networks in a built environment context, a case study is presented. The case study investigates the networks in one project team in a planning and engineering consultancy, employing 5,500 people worldwide.

The network analysis revealed a problem with the project caused not by a widespread failure in social networks, but the isolated failure of one or two sub‐networks; however, these had a major impact on the performance of the project as a whole. The cause of this failure, while not clear, can be postulated as being in some part due to the lack of a collaborative culture across disciplines. Multi‐disciplinary projects are vulnerable because, while most (disciplinary) teams may function well, failure of just one will jeopardise the project as a whole – a chain is only as strong as the weakest link. Mechanisms do not exist for addressing failure, mid‐project, in social networks. The project‐centric nature of the sector is also an issue, in that project teams are in a constant state of flux with relationships being established, then staff moving on and a new set of relationships being developed. This does not support long‐term stable and trusting relationships.

The paper concludes that the use of SNA techniques has practical benefits for inter‐ and intra‐transfer of knowledge and information among team members.