Cross‐layer routing protocol for event‐driven M2M communication in IoT‐assisted Smart City Planning and Management: CWSN‐eSCPM

Abstract

The robustness of wireless sensor networks (WSNs) has broadened the use of wireless communication systems for modern machine-to-machine (M2M) communication. Internet-of-Things (IoT) being one of the most explored technologies has gained wide attention due to low-cost WSN communication systems. IoT is one of the dominant technologies used in Smart City implementation demands better communication paradigm including mobility assisted transmission paradigms; however, native IEEE 802.15.4 standard does not have mobility provision that limits its use for real-time applications. On contrary, implementing mobility with classical WSN might impose significantly great topological changes and node or network condition variations which cannot be dealt with the traditional reactive routing approach. Incorporating node and/or network awareness with proactive network management can enable classical WSN to support mobility, which can be vital for IoT based Smart City Planning and Management (SCPM). With this motivation, in this study, a robust Cross-Layer Architecture based WSN Routing Protocol for Event-Driven M2M Communication in SCPM (CWSN-eSCPM) is developed. CWSN-eSCPM encompasses Proactive Node Management Strategy, Data-Centric Service Differentiation and Fair Resource Scheduling (DCSDFRS), Packet Velocity Estimation, Cumulative Congestion Estimation, Dynamic Link Assessment that enables optimal Best Forwarding Node selection for deadline sensitive and reliable data communication. CWSN-eSCPM applies dynamic link quality, cumulative congestion degree, and packet velocity of a node to enable optimal routing decision. DCSDFRS enables optimal resource provision to the real-time data while assuring maximum possible resource for non-real-time data that assures Quality of Service provision for event-driven critical communication in SCPM. Noticeably, CWSN-eSCPM protocol has been applied on top of IEEE 802.15.4 protocol standard, while preserving backward compatibility feature, it can be used for WSN assisted communication purposes. The authors' proposed protocol has performed better in terms of packet delivery ratio, packet loss ratio and deadline miss ratio for both real-time data as well as non-real-time data.

Enhanced reinforcement learning assisted dynamic power management model for internet‐of‐things centric wireless sensor network

“…On the other hand, realising the significance of WSN towards IoT enabled SCPM purposes, augmenting energy, and resource efficiency, are of utmost significance. Recalling our previous contribution [16], where a highly robust mobile‐WSN routing approach was developed to meet QoS centric communication for IoT‐based SCPM, this research introduces a supplementary contribution to enhance power management and QoS‐centric transmission scheduling. Although we considered cross‐layer information over standard IEEE 802.15.4 protocol stack, which assured reliable and QoS‐centric communication, it could not address PHY switching and network‐adaptive transmission scheduling.…”

“…The proposed DPM model primarily addresses QoS‐centric and resource‐efficient transmission scheduling problem in mobile‐WSN [16], so as to support IoT enabled SCPM purposes. With this motive, the proposed model has been applied over the WSN standard IEEE 802.15.4 protocol stack, where the information from both the system layer and the physical (PHY) layer has been applied to perform stochastic prediction followed by dynamic switching control.…”

“…This, as a result, can strengthen IoT to play a vital role in real‐world data collection, control and dissemination across the SCPM network. In our previous work [16], we designed a cross‐layer architecture‐based reliable and QoS centric routing model for the IoT ecosystem. Although our proposed model achieved better efficiency towards QoS provision, especially for high throughput, buffer management and reliable transmission, it could not address DPM issues under dynamic topology condition or non‐linear network condition.…”

“…Obtaining the initial network state‐activity parameters (say, known parameters), our proposed model employs the Markov chain prediction (MDP) concept to obtain the unknown network parameters and amalgamating the known and unknown network parameters or state‐activity values [in later discussion called state‐activity/value function (SVF)], we performed reinforcement learning that intended to maintain low‐cost function (here, we considered HC, BC, BEP/BER as the cost function to be minimised), while it intended to ensure optimal switching control or dynamic PHY switching decision to assist maximum resource utilisation. Thus, the overall proposed model intended to achieve QoS centric transmission with high‐reliability and optimal resource utilisation to strengthen CWSN‐eSCPM [16] towards realistic IoT assisted SCPM solution. Noticeably, the classical reinforcement learning algorithm, such as Q‐learning [17, 18], operates or learns merely on the known parameters which can be suitable only for the static network.…”

“…Since our proposed DPM model employs both known and unknown parameters, it ensures suitability over mobile‐WSN to yield QoS communication for IoT ecosystems such as SCPM. Thus, the proposed model contributes a novel and robust stochastic prediction assisted DPM mechanism to support QoS and optimal resource utilisation for CWSN‐eSCPM [16]. This, as a result, can contribute a robust IoT communication ecosystem for SCPM purposes.…”