A huge increase in the percentage of the world’s urban population poses resource management especially energy management challenges in smart cities. In this paper, the growing challenges of energy management in smart cities have been explored and significance of elimination of energy holes in converge cast communication has been discussed. The impact of mitigation of energy holes on the network lifetime and energy efficiency has been thoroughly covered. The particular focus of this work has been on energy-efficient practices in two major key enablers of smart cities namely, the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). In addition, this paper presents a robust survey of state-of-the-art energy-efficient routing and clustering methods in WSNs. A niche energy efficiency issue in WSNs routing has been identified as energy holes and a detailed survey and evaluation of various techniques that mitigate the formation of energy holes and achieve balanced energy-efficient routing has been covered.

Due to the inherent property of the processing resource request from mobile active or passive devices as part of internet of things (IoT), processing capacity as well as latency become major optimization criteria. To achieve overall optimized uses of cloud resources - having dynamic tracking, monitoring as well as orchestration framework is one of the key challenges to overcome. In the same context, enhanced uses of computing devices at distributed location is predicted to facilitate the success of IoT; subsequently the success of fifth generation (5G) of Wireless technologies. This opens enormous potential to integrate the unused resources of such distributed computed devices within the conventional cloudlet or cloud federation. However, this requires an efficient micro-level distributed computing resource tracking, monitoring and orchestration; where resources are distributed in geo-location as well as the availability of unused resources are time variant in nature. In this paper, we have proposed a cognitive edge-computing based framework solution for these requirements in order to achieve an efficient use of these distributed resources. This provides the end-user with a dynamic soft extension of computing facilities of cloudlet and cloud federation, as well as a revenue generation avenue to end-user. The simulation results show that such extension can be an exponential function of the number of local processing platforms agreed to participate in the proposed cognitive resource sharing.

Electric motor is an integral part of electric propulsion system in electrified vehicles. Choice of an appropriate electric motor and its speed and torque control strategy play an utmost significant step so as to get the required operational characteristics from the electric drive train. The aim of thsi paper is to review on different kinds of traction motors for electric vehicles (EVs) and hybrid electric vehicles (HEVs) and high transient performance speed sensorless control for traction drive. The heart of electric propulsion system, electric motor types for required operational characteristics and limitations are summarized from a system perspective with the latest improvements. For high performance applications such as EVs / HEVs, a brief review on speed sensorless control is presented. Different machine mathematical model based rotor speed estimation schemes for sensorless control are reviewed with respect to key features and operational limitations. Finally, this paper provides further research directions in the field of traction motor drive and its speed sensorless control.

A variety of skin tones along with other constraints on image capturing within a given background make face detection a challenging task of image processing for surveillance and security applications. In this study, a colour-based skin model is proposed for a diverse range of skin tones. It is found that the proposed skin model outperforms several existing skin models. The proposed skin model is used to develop a soft decision-based multiple face detection framework. The robustness of the proposed framework has been evaluated for a wide range of poses, facial expressions, complex backgrounds and blur noises. On the basis of the proposed skin model and the soft decision-based scheme, the range of values of Cb and Cr has been compressed to obtain reduced false detections and missed detections as compared with the existing algorithms.

Due to the technological advancements in wireless communication and their continuously increasing applications in collaborative and cooperative smart infrastructures, energy efficient data collection using wireless devices, has gained significant importance recently. Modern wireless sensor networks refer to network of low-powered and energy-constrained Internet of Things (IoT) devices. Although data collection using hierarchical routing with clustered network improves energy efficiency but introduces energy holes in the region closer to the data gathering center due to heavy relaying load on cluster heads. In this paper, first an improved data gathering center deployment technique for heterogeneous networks has been proposed. Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS), a multi-criteria decision-making technique, is used to determine the optimal location for the deployment of data gathering center. The proposed technique is adaptive to various shaped networks as required by IoT and increases energy efficiency. Secondly, an unequal clustering based on transmission distances has been proposed. Moreover, cubical, and spherical segmentation schemes for 3D heterogeneous networks have been proposed that assist the formation of unequal clusters. Finally, a shape independent data rate-based segmentation has been proposed that further extends the adaptability and scalability of the proposed unequal clustering. The results demonstrate that the proposed data traffic-based shape independent adaptive clustering scheme increases network lifetime up to 14.2% and 18.8% as compared to Fuzzy Logic based unequal clustering and IUCR respectively. It also reduces the overall network energy consumption up to 61.4% as compared to the state-of-the art unequal clustering methods.

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.