Smart City and Well-Being: Opinions by the Guest Editors

As with technology, the concept of the Smart City has evolved over time in line with digitisation processes and the changing needs of cities and their inhabitants. Indeed, it was in the early 1980s when discussions first arose regarding the role that information technology would play in the development of conventional urban activities [1–3]. Some thirty years later, in 2009, the concept of the Smart City was first defined when, in Rio de Janeiro, a plan came into effect that employed technological innovation and waste management to improve the quality of life in the city by minimizing wastage [4]. This is a true evolution in which the vision of the traditional city is superseded by a more modern urban reality creating an ideal, highly automated ecosystem in which Information and Communication Technologies (ICT) take on the role of the core infrastructure of a Smart City [5–7]. The technological and techno-centric revolution, currently dictated by the market, may, however, result in a decrease in inclusivity and at the same time an increase in the digital divide. Moreover, a Smart City that is too heavily based on technological solutions runs the risk of becoming disconnected from policies with a real impact on urban contexts [8]. The term ‘Smart City’ encapsulates a conception of urban reality that transcends technological boundaries and aims to raise the standards of sustainability, liveability and economic dynamism of the cities of the future [9,10]

Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method

IETF has proposed the routing protocol for low power and lossy networks (RPL) for IOT as view as light weight routing protocol. In RPL, the objective function (OF) is used to select the best route between child and root node. Several researches have been conducted in order to, enhance OF according to number parameters such as number of hops, remaining energy and expected number of transmissions (ETX), without a consideration to other challenges such as congestion node problem and latency. So, to overcome these challenges a new technique called “Enhance-Minimum Rank with Hysteresis Objective Function (MHOF)” is proposed in this paper, to select the ideal path between the child and root node. The technique is consisted of three layers: parent selection layer in which parent is selected based on three parameters (ETX, RSSI and nodes’ residual energy), path selection layer in which the best route is chosen according to the minimum of (average ETX value) and maximum of (average remaining energy value) of all nodes in the selected route. The last layer is child node minimization, which utilized to solve the congestion node energy problem by using two parameters (RSSI reference and threshold value). The proposed method has been implemented and evaluated by using Cooja simulator software. The simulation results have shown that selected path with E-MHOF is increased the network lifetime and reduced latency in comparison with MHOF

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions

A hybrid objective function with empirical stability aware to improve RPL for IoT applications

The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stability aware (HOFESA), implemented in the network layer of the embedded operating system CONTIKI, which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption. Also, To remedy to frequent preferred parents changes problems caused by taking into account more than one metric, our proposal relies on static and empirical thresholds. The designed HOFESA, evaluated under COOJA emulator against Standard-RPL and EC-OF, showed a packet delivery ratio improvement, a decrease in the power consumption, the convergence time and DIO control messages as well as it gives network stability through an adequate churn

New RPL Protocol for IoT Applications

The Objective Function (OF) can be used by the Routing protocol for low power lossy networks (RPL) to construct a Destination Oriented Directed Acyclic Graph (DODAG) based on routing metrics. The standard OFs suffer from long hops when selecting the route, which may cause consume the node\u27s energy faster. In this paper, we suggest an improvement of RPL OF that considers three metrics. The results show that the proposed protocol increases network lifetime by reducing energy consumption, increasing efficiency, increasing Packet Delivery Ratio (PDR), and decreasing packet loss ratio. In terms of PDR, packet loss ratio, and average power consumption, the best performance of the proposed protocol is shown in the network with 70 nodes and when the transmission range is 50m. Compared with the MRHOF, the proposed protocol increased the PDR by 58.425%, decreased the packet loss ratio by 0.21765, and decreased the total power consumption by 181.815mW. In terms of the average Expected Transmission Count (ETX) the best performance of the proposed protocol is shown in the network with 60 nodes and the transmission range is 40m. The proposed protocol reduced the average ETX by 49 compared to the MRHOF

DDSLA-RPL: Dynamic Decision System Based on Learning Automata in the RPL Protocol for Achieving QoS

The internet of things is a worldwide technological development in communications. Low Power and Lossy Networks (LLN) are a fundamental part of the internet of things with numerous monitoring and controlling applications. This network has many challenges as well, due to limited hardware and communication resources, which causes problems in applications such as routing, connections, data transfer, and aggregation between nodes. The IETF group has provided a routing model for LLN networks, which expands IPv6 protocol based on Routing Protocol (RPL). The pro-posed decision system DDSLA-RPL creates a list of limited k member optimal parents based on qualitatively effective parameters such as hop, link quality, SNR rate, and ETX energy consumption, by informing child nodes of their connection link to available parents. In the routing section, a decision system approach based on learning automata has been proposed to dynamically determine and update the weight of influential parameters in routing. The effective parameters in the routing phase of DDSLA-RPL include battery depletion index, connection delay, and node queuing and throughput. The results of the simulation show that the proposed method outperforms other methods by about 30, 17, 20, 18, and 24 percent in mean longevity and energy efficiency, graph sustainability, operational power and latency, packet delivery rate test, and finally number of control messages test, respectively

Enhanced priority-based adaptive energy-aware mechanisms for wireless sensor networks

Wireless Sensor Networks (WSN) continues to find its use in our lives. However, research has shown that it has barely attained an optimal performance, particularly in the aspects of data heterogeneity, data prioritization, data routing, and energy efficiency, all of which affects its operational lifetime. The IEEE 802.15.4 protocol standard, which manages data forwarding across the Data Link Layer (DLL) does not address the impact of heterogeneous data and node Battery-Level (BL) which is an indicator for node battery life. Likewise, mechanisms proposed in the literature – TCP-CSMA/CA, QWL-RPL and SSRA have not proffered optimal solution as they encourage excessive computational overhead which results in shortened operational lifetime. These problems are inherited on the Network Layer (NL) where data routing is implemented. Mitigating these challenges, this research presents an Enhanced Priority-based Adaptive Energy-Aware Mechanisms (EPAEAM) for Wireless Sensor Networks. The first mechanism is the Optimized Backoff Mechanism for Prioritized Data (OBMPD) in Wireless Sensor Networks. This mechanism proposed the Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). The CSTP-MAC is implemented on the DLL. In this mechanism, unique backoff period expressions compute backoff periods according to the class and priority of the heterogeneous data. This approach improved network performances which enhanced network lifetime. The second mechanism is the Shortest Path Priority-Based Objective Function (SPPB-OF) for Wireless Sensor Networks. SPPB-OF is implemented across the NL. SPPB-OF implements a unique shortest path computation algorithm to generate energy-efficient shortest path between the source and destination nodes. The third mechanism is the Cross-Layer Energy-Efficient Priority-based Data Path (CL-EEPDP) for Wireless Sensor Networks. CL-EEPDP is implemented across the DLL and NL with considerations for node battery-level. A unique mathematical expression, Node Battery-Level Estimator (NBLE) is used to estimate the BL of neighbouring nodes. The knowledge of the BL together with the priority of data are used to decide an energy-efficient next-hop node. Benchmarking the EPAEAM with related mechanisms - TCP-CSMA/CA, QWL-RPL and SSRA, results show that EPAEAM achieved improved network performance with a packet delivery ratio (PDR) of 95.4%, and power-saving of 90.4%. In conclusion, the EPAEAM mechanism proved to be a viable energy-efficient solution for a multi-hop heterogeneous data WSN deployment with support for extended operational lifetime. The limitations and scope of these mechanisms are that their application is restricted to the data-link and network layers, moreover, only two classes of data are considered, that is; High Priority Data (HPD) and Low Priority Data (LPD)

Sigma Routing Metric for RPL Protocol

This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption

Sigma Routing Metric for RPL Protocol

This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption