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This paper aims to develop generic strategies for improving energy consumption for location sensing on smartphones and compares the results of iOS and Android implementations. Mobile smartphone applications utilizing localization sensors (e.g. Global Positioning System) collectively face the problem of battery draining. Energy consumption is at a peak when applications permanently and stolidly use those sensors, even if their excessive exploitation is avoidable (e.g. when the user carrying the device is not moving).

Considering contextual parameters affecting localization of mobile devices (i.e. incorporating movement probability, speed, etc.) is the basic idea for developing a strategy capable of reducing energy consumption for location determination on mobile devices. This paper explains the paradigm and draws the architecture for a generic context-based energy saving strategy for mobile location-based services.

The paper reveals the positive implications in terms of energy consumption measured in the course of exhaustive tests for iOS and Android devices and discusses accuracy issues and potential workarounds, especially focusing on Apple’s M7 motion co-processor for consuming accelerometer data on a low energy level.

The paper identifies and measures energy issues for location determination on smartphones and presents a generic and heuristic concept for saving energy.

Detecting and tracking the position of a mobile user has become one of the important subjects in many mobile applications. Such applications use location based services (LBS) for learning and training user movements in different places (cities, markets, airports, stations) along different modes of transport (bus, car, cycle, walk). To date, GPS is the key solution to all LBS but repeated GPS querying is not economical in terms of the battery life of the mobile phone. The purpose of this paper is to study how cheap and energy‐efficient air pressure sensors measuring the altitude could be used, as a complement to the dominant GPS system. The location detection and route tracking task is then accomplished by matching the collected altitude traces with the altitude curves of stored data to find the best matching routes.

The cornerstone of the authors' approach is that a huge amount of route data, collected with GPS devices, is available in various cloud services. In order to evaluate the mechanism of matching routes with altitude data, the authors build a prototype system of crowd‐sourced database containing only altitude data of different routes along different modes of transport. How accurately this stored altitude data could be matched with the collected altitude traces is the key question of this study.

Results show that, within a certain level of accuracy, older repeated routes can be detected from newly tracked altitude traces. Further, the level of accuracy varies depending on the length of path traversed, route curvature, speed of travel and sensor used for tracking.

The new contribution in this paper is to propose an alternative route detection mechanism which minimizes the use of GPS query. This concept will help in retrieving the GPS coordinates of already traversed routes stored in a large database by matching them with currently tracked altitude curves.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

The purpose of this paper is to propose a receding horizon control approach for the problem of locating unknown wireless sensor networks by using a mobile robot.

A control framework is used and consists of two levels: one is a decision level, while the other is a control level. In the decision level, a spatiotemporal probability occupancy grid method is used to give the possible positions of all nodes in sensor networks, where the posterior probability distributions of sensor nodes are estimated by capturing the transient signals. In the control level, a virtual robot is designed to move along the edge of obstacles such that the problem of obstacle avoidance can be transformed into a coordination problem of multiple robots. On the basis of the possible positions of sensor nodes and virtual robots, a receding horizon control approach is proposed to control mobile robots to locate sensor nodes, where a temporary target position method is utilized to avoid several special obstacles.

When the number of obstacles increases, the average localization errors between the actual locations and the estimated locations significantly increase.

The proposed control approach can guide the mobile robot to avoid obstacles and deal with the corresponding dynamical events so as to locate all sensor nodes for an unknown wireless network.

At the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.

In this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.

This study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.

As per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

With the rapid growth of wireless sensor networks (WSNs), they have become an integral and substantial part of people's life. As such WSN stands as an assuring outlook, but because of sensor's resource limitations and other prerequisites, optimal dual route discovery becomes an issue of concern. WSN along with central sink node is capable of handling wireless transmission, thus optimizing the network's lifetime by selecting the dual path. The major problem confronted in the application of security mechanisms in WSNs is resolving the issues amid reducing consumption of resources and increases security.

According to the proposed system, two metrics, namely, path length and packets delivery ratio are incorporated for identifying dual routes amid the source and destination. Thereafter by making use of the distance metric, the optimal dual route is chosen and data transmission is carried out amid the nodes. With the usage of the recommended routing protocol high packet delivery ratio is achieved with reduced routing overhead and low average end to end delay. It is clearly portrayed in the simulation output that the proposed on demand dual path routing protocol surpasses the prevailing routing protocol. Moreover, security is achieved make use of in accord the data compression reduces the size of the data. With the help of dual path, mathematical model of Finite Automata Theory is derived to transmit data from source to destination. Finite Automata Theory comprises Deterministic Finite Automata (DFA) that is being utilized for Dual Path Selection. In addition, data transition functions are defined for each input stage. In this proposed work, another mathematical model is 10; introduced to efficiently choose an alternate path between a receiver and transmitter for data transfer with qualified node as relay node using RR Algorithm. It also includes Dynamic Mathematical Model for Node Localization to improve the precision in location estimation using Node Localization Algorithm. As a result a simulator is built and various scenarios are elaborated for comparing the performance of the recommended dual path routing protocol with respect to the prevailing ones.

Reliability and fault-tolerance: The actual motive in utilizing the approach of multipath routing in sensor network was to offer path resilience in case of a node or link failures thus ascertaining reliable transmission of data. Usually in a fault tolerant domain, when the sensor node is unable to forward the data packets to the sink, alternative paths can be utilized for recovering its data packets during the failure of any link/node. Load balancing: Load balancing involves equalizing energy consumption of all the existing nodes, thereby degrading them together. Load balancing via clustering improves network scalability. The network's lifetime as well as reliability can be extended if varied energy level's nodes exist in sensor node. Quality of service (QoS): Improvement backing of quality of service with respect to the data delivery ratio, network throughput and end-to-end latency stands very significant in building multipath routing protocols for various network types. Reduced delay: There is a reduced delay in multipath routing since the backup routes are determined at the time of route discovery. Bandwidth aggregation: By dividing the data toward the same destination into multiple streams (by routing all to a separate path) can aggregate the effective bandwidth. The benefit being that, in case a node possesses many links with low bandwidth, it can acquire a bandwidth which is more compared to the individual link.

Few more new algorithms can be used to compare the QoS parameters.

Proposed mechanism with feedback ascertains improvised delivery ratio compared to the single path protocol since in case of link failure, the protocol has alternative route. In case there are 50 nodes in the network, the detection mechanism yields packet delivery of 95% and in case there are 100 nodes, the packet delivery is lowered to 89%. It is observed that the packet rate in the network is more for small node range. When the node count is 200, the packet ratio is low, which is lowered to 85%. With a node count of 400, the curve depicts the value of 87%. Hence, even with a decrease in value, it is superior than the existing protocols. The average end-to-end delay represents the transmission delay of the data packets that have been successfully delivered as depicted in Figure 6 and Table 3. The recommended system presents the queue as well as the propagation delay from the source to destination. The figure depicts that when compared to the single path protocol, the end-to-end delay can be reduced via route switching. End-to-end delay signifies the time acquired for the delay in the receival of the the retransmitted packet by each node. The comparison reveals that the delay was lower compared to the existing ones in the WSN. Proposed protocol aids in reducing consumption of energy in transmitter, receiver and various sensors. Comparative analysis of energy consumptions of the sensor in regard to the recommended system must exhibit reduced energy than the prevailing systems.

On demand dual path routing protocol. Hence it is verified that the on demand routing protocol comprises DFA algorithms determines dual path. Here mathematical model for routing between two nodes with relay node is derived using RR algorithm to determine alternate path and thus reduce energy consumption. Another dynamic mathematical model for node localization is derived using localization algorithm. For transmitting data with a secure and promising QoS in the WSNs, the routing optimization technique has been introduced. The simulation software environment follows the DFA. The simulation yields in improvised performance with respect to packet delivery ratio, throughput, average end-to-end delay and routing overhead. So, it is proved that the DFA possesses the capability of optimizing the routing algorithms which facilitates the multimedia applications over WSNs.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

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.

In particular, this paper aims to systematically analyze a few prominent wireless sensor network (WSN) clustering routing protocols and compare these different approaches according to the taxonomy and several significant metrics.

In this paper, the authors have summarized recent research results on data routing in sensor networks and classified the approaches into four main categories, namely, data-centric, hierarchical, location-based and quality of service (QoS)-aware, and the authors have discussed the effect of node placement strategies on the operation and performance of WSNs.

Performance-controlled planned networks, where placement and routing must be intertwined and everything from delays to throughput to energy requirements is well-defined and relevant, is an interesting subject of current and future research. Real-time, deadline guarantees and their relationship with routing, mac-layer, duty-cycles and other protocol stack issues are interesting issues that would benefit from further research.