This work proposes an optimal pricing scheme– OPUS– for UAV-as-a-Service (UaaS) using a bargaining game. In a UaaS platform, multiple actors participate– end-users request services, UAV owners lease their UAVs, and a centralized service provider allocates UAVs equipped with heterogeneous onboard sensors. A UAV can support multiple applications by moving across task locations, where both its mobility and the operations of its onboard sensors contribute to overall energy consumption. Consequently, energy use directly affects the price charged to end-users. On the other hand, the service provider aims to maximize revenue, while end-users seek to pay a minimum chargeable price without compromising the service quality. Thus, the challenge is to design an efficient pricing mechanism that balances these conflicting objectives. Therefore, in this work, we aim to design a pricing scheme, OPUS, capable of determining an optimal chargeable price by jointly considering energy consumption, time of use, resource utilization, and UAV reputation. Specifically, time-based pricing accounts for the duration of UAV engagement, resource-based pricing captures operational overheads such as transmission and storage, and reputation-based pricing reflects UAV reliability and past performance. We formulate the problem using the Rubinstein bargaining model to ensure a fair trade off between the service provider's profit and the end-user's satisfaction. Extensive experimental results demonstrate that OPUS outperforms existing schemes– QUEST, TMSC, and Yu's strategy– for UaaS. Specifically, OPUS improves service provider profit by an average of 33.7%.

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform.

In this work, we propose a UAV Service Hand-off scheme (Serv-HU) for the UAV-as-a-Service (UaaS) platform to provide seamless UAV services to the end-users. Traditionally, a service provider of a UaaS platform serves a limited application area due to the unavailability of adequate resources such as UAVs. Failing to deliver the service by the service providers for the requested entire application area by the end-user affects the reputation of the service providers. Consequently, the service delivery for a partial application area impacts the overall business, which is unacceptable for a Service-Oriented Architecture. To address this issue, we design a service hand-off scheme that enables the service providers to serve the entire requested application area by the end users with the help of other available service providers. We consider the presence of two types of service providers – Primary (PSP) and Secondary (SSP) in a UaaS platform. We apply a two-stage approach for the UAV service delivery to the end-users. In the first stage, a PSP optimally selects the SSPs for serving the uncovered application area by the PSP. The end-users request the service from the PSP, and on failing to provide the service for the entire application area, the PSP makes the service available from the optimally selected SSPs. In the second stage, we design an optimal pricing strategy that helps in determining the price charged to the end-users considering the involvement of PSPs and SSPs. We apply the Lagrangian multiplier method and Karush-Kuhn-Tucker (KKT) conditions to achieve the outcomes of these two stages. The simulation results depict that the charged price is reduced by$10.3 - 12.7\%$while we apply the optimal SSP selection strategy as compared to the random selection of SSPs.

This work proposes a novel customer segment-based pricing scheme, CuPric, tailored for Mobile Sensors-as-a-Service (mSe-aaS) architecture for IoT Applications. mSe-aaS offers flexible and scalable mobile sensor services for different IoT applications. However, multiple actors involved in an mSe-aaS architecture participate in order to earn profit or access services. Therefore, such financial transactions among the actors lead to the need to develop an optimal pricing scheme. On the other hand, Customer segmentation is necessary to classify users based on their specific usage patterns and resource requirements, ensuring customized service offerings and optimized pricing strategies. CuPric utilizes customer segmentation techniques, including Recency-Frequency-Monetary (RFM) analysis and K-Means clustering. We modify the RFM technique by replacing the monetary component with computation resources fitting into the context of mSe-aaS. Further, we model the proposed pricing problem as an optimization problem and apply the Lagrangian multipliers while ensuring the infrastructure is allocated efficiently, service provider expenses are covered, and the Sensor-Cloud Service Provider (SCSP) profit is maximized. The proposed scheme, CuPric, adapts to varying resource demands and provides insights into user behavior, leading to more strategic infrastructure allocation and enhanced overall service efficiency. Through extensive simulations, we demonstrate that introducing customer segmentation earns at least 37% better profit. Additionally, 95% of users are charged less than half the maximum price charged to any user in the CuPric.

Due to the lack of flexible analytical and traffic measurement modules, the current 6TiSCH network architecture is deficient for use in low-latency and reliable control-loop communications. In this paper, we propose a programmable scheme, PRC6, for use over the 6TiSCH network to support low latency and reliable communications. PRC6 provisions an edge-based programming mechanism, which is capable of (i) programming various application-specific tasks, (ii) reconfiguring the 6TiSCH schedule as per the requirements of tasks, and (iii) scheduling deadline-aware forwarding at the SDN core network. The programmability feature of PRC6 offers flexibility in the execution of decision-making and analytics for improved actuation in the associated control-loops. By reconfiguring 6TiSCH-related parameters, PRC6 establishes low-latency communication between the sensor and actuator nodes. We define a new header type to enable interoperable packet delivery between the Low Power Lossy Networks (LLNs) and the core network.

In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by 75% compared to the traditional UaaS platform.

This work proposes a Fog-enabled Mobile Sensor (FoMS) virtualization platform for serving Internet of Things (IoT) applications. Traditionally, a Mobile Sensor-Cloud (MSC) architecture uses the concept of mobile sensor virtualization and provisions mobile Sensors-as-a-Service (mSe-aaS) to serve multiple IoT applications. However, the existing architecture of MSC does not consider time-critical IoT applications such as transportation, industry, and rescue management. Therefore, considering these time-critical applications, we propose the FoMS architecture, which is capable of reducing overall service latency for an application. In FoMS, we adopt the inherent characteristics - of handling service latency - of fog computing in MSC architecture to provide virtualized mSe-aaS in the stipulated time. As fog computing is new to an MSC architecture, the existing fog node selection schemes are unsuitable for FoMS. Therefore, we design a scheme for selecting a suitable fog node for FoMS and proposed an optimal pricing strategy for the selected fog node. We depict the effects on the utility and price for the different parameters we considered.

In this article, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units: 1) a portable healthcare unit and 2) an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and interunit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention toward IoT-based healthcare technologies.

In this work, we propose an optimal UAV selection scheme (Opti-U) for the UAV-as-a-Service platforms to offer persistent services to the end-users. In such a platform, multiple UAVs work synchronously and collaboratively to serve different applications. On the other hand, the end-users spend a certain amount to receive uninterrupted and seamless services from the platform. Typically, the UAVs are incapable to serve an application continuously for a long duration due to their energy-constraint nature. Therefore, when the energy level of a UAV drops to a certain threshold value, an alternative suitable UAV is necessary to be selected for continuing uninterrupted and seamless services to the end-users. In a UAV-as-a-Service platform, a UAV hosts multiple sensor nodes for serving different applications. All the UAVs present in a platform may not be suitable to serve all types of applications due to the lack of appropriate sensors in them. Moreover, different UAVs have diverse capabilities of hovering, processing, and communicating. Consequently, the UAV selection mechanism becomes a challenging and important issue, which is unaddressed in the current literature, in a UAV-as-a-Service platform. The proposed scheme, Opti-U, selects an optimal UAV, among the available ones, considering their capabilities and utilization factor in a UAV-as-a-Service platform.

This article introduces the concept of context-aware attribute learning with cipher policy-attribute-based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled Society 5.0. The concept of Society 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserves privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely, attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named the context-aware attribute learning scheme (CASE), which autonomously learns users’ contextual information, exploiting edge intelligence, generates attributes, and reduces the post-encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over smart healthcare systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32%–33% average network delay, 33%–35% average energy consumption, and 31%–36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using the support vector machine (SVM), decision tree (DT), and naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.

The presence of dumb nodes in sensor-cloud environment leads to degraded system performance. In this article, we consider the presence of dumb nodes in the sensor-cloud platform, and thereafter, propose a dynamic pricing scheme, while considering the existence of such nodes in the networks. The existing literature addresses the problem of pricing in sensor-cloud with the assumption of an ideal environment with normally functioning sensor nodes. The proposed pricing model considers the realistic existence of dumb nodes in sensor-cloud platforms. Further, the dumb behavior of a sensor node is dynamic in nature, as it is dependent on environmental conditions such as the occurrence of heavy rainfall, high temperature, and the presence of fog. However, in the absence of such adverse environmental conditions, the erstwhile dumb nodes resume normal behavior. The permanent removal of a dumb node from sensor-cloud is not always a feasible solution. When a dumb node is assigned to a virtual sensor, the existing pricing scheme in sensor-cloud charges same as other normal nodes. Thus, in such a situation, a user pays the normal price for a dumb node to the Sensor-Cloud Service Provider (SCSP). Consequently, the sensor owner of dumb node earn same profit as the owner of a normal node. Therefore, we formulate a scheme forDynamic pricing in sensor-cloud environment in the presence of dumb nodes(DISCLOUD). As the presence of dumb nodes in sensor-cloud affects the Quality of Service (QoS), we propose a scheme considering QoS of the sensor-cloud. The proposed scheme, DISCLOUD, enables profit maximization of the SCSP, while considering the price required to be paid by end-user based on QoS.

This article proposes an optimal pricing scheme for provisioning sensors-as-a-service (Se-aaS) for catering to applications with multi-tenancy requirements in a sensor-cloud platform. The scheme orchestrates a trade-off analysis between communication range and price in a sensor-cloud platform with range-reconfigurable nodes. The proposed scheme consists of two phases – (a) selection of a neighbor node of a source node, and determination of optimal price for the selected neighbor node. In the first phase, a source node adjusts its communication range and selects its best possible neighbor node usingselectivity factorof all the neighbor nodes. The selectivity factor considers the determinants such as effective residual energy, effective power consumption, and the number of applications to which the neighbor nodes are associated in the neighbor selection process. In the second phase, we design a utility function to determine the optimal price of the selected neighbor node. We use theLagrangianfunction to model the proposed problem as a mixed-integer linear program (MILP) and obtain the optimal solution using the Karush-Kuhn-Tucker (KKT) conditions. The existing works on pricing in sensor-cloud are deficient in considering the presence of the reconfigurable communication range of sensor nodes. Moreover, based on the value of the communication range, the charged price of the sensor nodes varies. Thus, in this article, we propose a pricing scheme with a trade-off of the reconfigurable communication range of sensor nodes and the charged price incurred in adjusting the communication range. Extensive experimental results show that the proposed scheme performs better compared to the existing pricing schemes for sensor-cloud. In precise, the proposed scheme is capable of increasing the average number of neighbor nodes by at least 1.38 percent. Further, the proposed scheme is capable of reducing the charged price by 10.55 percent, as compared to the existing pricing scheme, DOPH.

In this article, we propose a pricing scheme, named PRIME, for provisioningmobile Sensors-as-a-Service(mSe-aaS) in the mobile sensor-cloud (MSC) architecture, with an aim to optimally distribute the financial profit among different actors of MSC. Unlike traditional sensor-cloud, MSC introduces a new actor as device owner, whose mobile device hosts the physical sensor nodes. On the other hand, the device and sensor owners earn certain revenues, based on the usage of the sensor nodes and the mobile devices, for provisioning mSe-aaS to the end-users. MSC is a contemporary architecture, and therefore, no pricing scheme exists for it. In this work, we consider the presence of the device owner, sensor owner, Sensor-Cloud Service Provider (SCSP), and end-user to determine an optimal pricing strategy. In order to design such a strategy, we use theLagrangian multipliermethod and applyKarush-Kuhn-Tucker(KKT) conditions. On the other hand, an end-user has multiple options to select an SCSP among the available ones. Therefore, based on the reputation of all the available SCSPs, PRIME enables an end-user to select a suitable one. Extensive experimental results report that PRIME increases the profit of sensor and device owners by 25.67% and 29.12%, respectively. We also compare PRIME with an existing pricing scheme for traditional sensor-cloud architecture. We notice that the service return using PRIME increases by 55.31% as compared to the same using the traditional sensor-cloud architecture.

This paper introduces a scheme for autonomous service exchange among multiple sensor-cloud service providers (SCSPs) in a sensor-cloud (SC) platform for Internet of Things (IoT) applications. Typically, SC offers Sensors-as-a-Service (SeaaS) using the concept of sensor virtualization for serving different IoT applications seamlessly in real-time. On the other hand, an SC platform reduces the tasks of sensor deployment and management on the user by employing SCSP. In an SC platform, single SCSP may be incapable of serving an entire IoT application requested by an end-user due to the lack of sufficient sensor nodes (SNs) present in the region of interested of an application. However, the presence of multiple SCSPs in an SC platform plays a complementary role in serving an IoT application entirely by implementing the idea of service exchange among them. The proposed scheme, ServEx, enables service exchange among multiple SCSPs in an SC platform. In ServEx, we apply a 2-phase approach. In the first phase, we introduce the use of a data structure to store the service profile of end-users and SCSPs. On the other hand, in the second phase, we design a profile matching mechanism for enabling a SCSP to find desired SNs registered to other SCSPs. Through extensive experiments, we observe that ServEx reduces the average delay by 57% and the average energy consumption by 74% and increases the capability of complete service provisioning for each SCSPs.

In this paper, we propose SD-Reco, a Software-Defined Network (SDN)-based centralized AP and relay node re-configuration scheme for link recovery in IEEE 802.11ah networks. The proposed scheme identifies the overlapping regions and congestion of a network and re-configures Relays, APs, and SDN core nodes for reliable data transmission. Taking advantage of redundant links, the SDN controller triggers a node to receive/transmit frames using the best relay/AP for reliable and low latency delivery. The stations use redundant available links to relay/AP for any failure, not meeting the Quality of Service (QoS) requirement and congestion. Our solution use a relay placement scheme to know topology of the network for centralized controlling. An AP node determines the congestion status of each relay and itself and updates the same to the SDN controller. Accordingly, the controller selects an AP/relay and places flow-rules from overlapping regions for forwarding the traffic dynamically. SD-Reco improves packet delivery ratio up to 18.7% and latency up to 33.3%, compared to the existing state-of-the-art.

Due to the large-scale deployment of mobile stations, relays, and Access Points (APs), IEEE 802.11ah brings challenges that cannot be effectively addressed in a distributive manner. In this paper, we propose an uplink traffic-aware dynamic association and channel control mechanism for improving multi-hop throughput and delay performances. The proposed software-defined access network estimates throughput for relays and APs considering the stations’ probability of successful transmission. A centralized controller uses the estimated throughput for better decisions in association and channel selection in relays and APs. For improving the capacity of a relay or an AP, a utility function is dynamically measured from the available channels and bandwidth. The proposed association control mechanism allows for seamless handoff throughout the network. Overall, the proposed protocol improves throughput up to 10% and delay up to 13% as compared to the traditional scheme.

The frames captured by the camera should be of sufficient quality, generally measured in pixel density on a target (pixels per foot or pixels per meter (ppm)) to derive information suitable for social behavior analysis. Achieving 24x7 high pixel density coverage incurs a large number of cameras and exorbitant network bandwidth for large scale deployments. In this paper, we introduce additional features to the Camera Network-as-a-Service (CNaaS) concept proposed by Misra et al. to address two aspects pertinent to services related to social behavioral analysis. We present a Quality of Information-Aware (QoI-Aware) CNaaS to address the two aspects. (1) To allow the reuse of cameras which are already available in a locality to minimize the number of additional cameras required to provide the CNaaS service. (2) Inclusion and exclusion of camera node(s) into the CNaaS platform while ensuring fair opportunities to the Camera Network Owners (CNOs) which provide the same Quality of Information (QoI). The simulation results indicate that the proposed scheme is fair to nodes providing the same QoI, reduces the energy consumption, and excludes the cameras which offer lesser QoI. This will lead to an improvement in the usage of network bandwidth, and the profit of the Camera Network Service Provider (CNSP).

Sensor-cloud architecture is a wireless sensor network (WSN)-based Service-Oriented Architecture (SOA), in which a Sensor-Cloud Service Provider (SCSP) obtains WSNs on rental basis from multiple sensor-owners and provides these resources to the users in the form of chargeable units of services, termed as Sensors-as-a-Service (Se-aaS). A fraction of the revenue earned by the SCSP from the users is distributed among the oligopolistic sensor-owners for the usage of their nodes. Due to the inter-dependency among the sensor-owners for Se-aaS provisioning, selfish sensor-owners behave dishonestly to gain higher profits, thereby degrading the overall QoS. Existing works on sensor-cloud fail to address this issue. Hence, in this work, we propose DETER, a dynamic trust enforcing pricing scheme, which enforces trust among the selfish sensor-owners while ensuring profits for the SCSP.

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient's sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient's LPU and the hospital's server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

The proposed work relates to the development ofBig-Sensor-Cloud Infrastructure(BSCI) that immensely enhances the usability and management of the physical sensor devices. Traditional Wireless Sensor Networks (WSNs) are manufactured in a proprietary, vendor-specific design. Thus, the renderability of WSNs is almost infeasible to people/organizations that do not own a network of their own. Thus, in the existing system, WSN-based applications are inaccessible to the naive-users or common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional WSNs. However, with the increasing growth in the velocity, variety, and variability of data, the management becomes a serious concern and difficulty. Thus, existing systems are not able to capture, analyze, and control the present data efficiently, in real-time. BSCI is a distributed framework for “Big” sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The methods of the proposed BSCI are persuasive as they are equipped with the ability to handle “Big” data with enormous heterogeneous data volumes (in zettabyte) generated with tremendous velocity. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform. This data are processed and delivered to the end-users as a simple service – Sensors-as-a-Service (Se-aaS). BSCI completely maintains and manages the data and the metadata internally within its database. Multiple organizations with heterogeneous demand can be successfully served with Se-aaS through BSCI. From a user-perspective, BSCI is highly convenient as the users are completely abstracted from the underlying complex processing logic. This allows the naive users to envision the typical hardware sensor devices as simple accessible services like electricity, and water.

This paper explores the concept of AI-based Communication-as-a-Service (ACUTE) to reduce transmission delay and energy consumption, while transmitting data from end-devices to the cloud in the context of Society 5.0. Society 5.0 revolutionizes connected living with the help of a unified system that provides fully automated and end-to-end services, while addressing the demands of all the citizens or users in a society. On the other hand, 6G is one of the promising communication platforms that offers the communication requirements of Society 5.0 by provisioning dense network deployment and fast data delivery. Building Society 5.0 founded on the 6G architecture enables serialized data transmission in the connected living fabric by allowing a user to connect with an access point and transmit data over a single path. Without concurrent and intelligent data transmission, the communication framework of Society 5.0 increases network delay and overall energy consumption and affects the Quality-of-Service (QoS). To address these issues, we propose a solution founded on the concept of Communication-as-a-Service (CaaS), which offers an architecture to facilitate intelligent access point virtualization for enabling concurrency in data transmission across individual users in a 6G-enabled Society 5.0. In ACUTE, a virtual module (VM) employed at each edge device performs concurrent data transmissions by associating with a virtual access point (VAP), which is a set of access points optimally selected using Fuzzy C-Means. Thereafter, the VM forms a virtual path (VP), which maps to a set of paths between physical access points and VAPs. ACUTE distributes the data through the VAP and associated VP and randomizes data sequence for transmission across VP. Experimental results show that ACUTE outperforms the state-of-the-art while reducing the network delay by 27%, energy consumption by 95%, packet loss by 95%, and service cost by 26%.

The proliferation of IoT in various technological realms has resulted in the massive spurt of unsecured data. The use of complex security mechanisms for securing these data is highly restricted owing to the low-power and low-resource nature of most of the IoT devices, especially at the Edge. In this article, we propose to use blockchains for extending security to such IoT implementations. We deploy a Ethereum blockchain consisting of both regular and constrained devices connecting to the blockchain through wired and wireless heterogeneous networks. We additionally implement a secure and encrypted networked clock mechanism to synchronize the non-real-time IoT Edge nodes within the blockchain. Further, we experimentally study the feasibility of such a deployment and the bottlenecks associated with it by running necessary cryptographic operations for blockchains in IoT devices. We study the effects of network latency, increase in constrained blockchain nodes, data size, Ether, and blockchain node mobility during transaction and mining of data within our deployed blockchain. This study serves as a guideline for designing secured solutions for IoT implementations under various operating conditions such as those encountered for static IoT nodes and mobile IoT devices.

In this paper, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. Oligopolistic sensor-owners adopt unfair means to degrade the quality of service provided by other sensor-owners in the sensor-cloud market. In order to address this problem, a dynamic pricing scheme, named DETER, is proposed in this work to enforce trust among the sensor-owners for maintaining the quality of Se-aaS provided by the Sensor-Cloud Service Provider (SCSP). Each sensor node calculates distributed trust opinion for other nodes, while the SCSP calculates centralized trust opinion for each sensor-owner. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit. Thereby, using DETER, SCSP enforces high trust among the sensor-owners. Additionally, using DETER, energy consumption of sensor nodes in sensor-cloud decreases by 4.69-11.56 percent, and network overhead decreases by 52.6-56.53 percent. The trade-off between price earned by the sensor-owners and profit of the SCSP in service-oriented sensor-cloud is also maintained using DETER.

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.

This work proposes an optimal camera selection scheme, OptiCam, for provisioning Camera-Network-as-a-Service (CNaaS) to multiple end-users while ensuring fair participation of the camera-network owners. Typically, in CNaaS, multiple cameras work together to form a virtual camera network and provide the services to the end-users. These cameras are procured and deployed by their respective owners, who receive rents for lending their cameras. In such a situation, the camera selection mechanism must be fair, so that, each of the owners receives an equal opportunity to participate in the CNaaS platform and earn the profit. On the other hand, multiple camera nodes may be available to serve an end-user application. Therefore, it is pertinent to select the suitable camera nodes, among the available ones, and serve the application while ensuring a longer lifetime of the network. The proposed scheme, OptiCam, is capable of selecting suitable camera nodes, optimally, while ensuring the fair participation of the owners, and increasing the network lifetime. To formulate the proposed scheme, we use a market-based auction model. The results of extensive simulations show that the average lifetime is improved in the case of OptiCam as compared to normal camera network (NCN) by 34.4%. Additionally, we observed that the total number of cameras activated per unit time increases by 8.63% in the case of NCN, while it decreases by 70.77%, in case of OptiCam.

In this paper, we propose a scheme, OPTIVE, for obtaining the optimal configuration of a virtual sensor in the mobile sensor-cloud (MSC) architecture. The proposed scheme is capable of selecting the physical sensor nodes to form a virtual sensor (VS), based on the sensing area coverage for an application region. We use Markov Decision Process (MDP) to select the optimal mobile sensor nodes among the available ones, for configuring the VS in the application area. The MSC architecture is a new paradigm in which physical sensor nodes attain mobility by the virtue of mobile devices such as, laptops, cell phones, and vehicles. In MSC, a mobile device may move or exit from the application region at any time instant. Consequently, sensing hole arises in the application area, resulting in undesirable interruption in the end-user services. As multiple sensor nodes may be present in the application region, it is not suitable to allocate any available sensor node, randomly, to re-configure the VS for covering the sensing hole. In such a situation, OPTIVE selects the optimal physical sensor node to allocate in the VS for ensuring uninterrupted services to the end-users. Simulation results show that OPTIVE is capable of providing at least 80 - 90% coverage in the application area. Additionally, in the presence of 2 to 7 sensor nodes, the number of iterations in MDP change by 19.56%.

In this work, we propose a scheme for the formation of Dynamic Virtual Sensor for Overlapping Region (DIVISOR) in a IoT-based sensor-cloud platform. Practically, the interest of deployment area of similar sensor nodes by respective sensor owners may be the same, and consequently, the areas of coverage of the deployed sensor nodes of the different owners overlap with one another. Thus, in such a scenario, each of the sensor owners must get equal opportunity to earn profit from the deployment of their sensor nodes. Therefore, in order to provide an equal privilege to all sensor owners, we propose the scheme, DIVISOR, in order to form virtual sensors. This is one of the first attempts for the dynamic formation of virtual sensors, where the overlapping area of deployed sensor nodes by different sensor owners is considered. The experimental results demonstrate that the proposed scheme is energy-efficient and the average number of participating nodes increases with the increase in the total number of nodes in the network. Moreover, each sensor owner gets almost equal opportunity to rent their nodes.

In this paper, we present solution for the development of a novel infrastructure, safety-as-a-service (Safe-aaS) for the road transportation industry. Safe-aaS provides safety related decisions to the registered end-users. The safety decisions are customized as per the end-user types and their requirements. Existing related research work on road safety focus on the development of the safety systems, which are able to assist the driver of the vehicle. However, none of the works serves as a common platform for providing customized decisions dynamically as per user requirements. As per our knowledge, Safe-aaS is one of the first attempts in its domain, where multiple end-users receive safety related decision dynamically. An end-user enjoys the pay-per-use service of Safe-aaS, without concerning about the back-end process. Safe-aaS is based on service oriented architecture, where different business entities such as vehicle owners, sensor owners, safety service provider, and end-users are involved. We introduce the term, decision virtualization, which enables multiple end-users to access the customized decisions remotely. We present possible cost analysis for the entities involved in the system. Analytical results show the cost and profit analysis of the different entities. We observe the profit gain by mobile sensor owner is 19.69% more as compared to static sensor owner. In the presence of 5, 10, and 15 end-users, payable rent varies between 15%-20%. Additionally, we present two case studies to depict a clear view of usage of Safe-aaS.

In this work, the problem of topology control for self-adaptation in stationary Wireless Sensor Networks (WSNs) is revisited, specifically for the case of networks with a subset of nodes having temporary connection impairment between them. This study focuses on misbehaviors arising due to the presence of\enskip “dumb” nodes [Misra et al. 2014; Roy et al. 2014a, 2014b, 2014c; Kar and Misra 2015], which can sense its surroundings but cannot communicate with its neighbors due to shrinkage in its communication range by the environmental effects attributed to change in temperature, rainfall, and fog. However, a dumb node is expected to behave normally on the onset of favorable environmental conditions. Therefore, the presence of such dumb nodes in the network gives rise to impaired connectivity between a subset of nodes and, consequently, results in change in topology. Such phenomena are dynamic in nature and are thus distinct from the phenomena attributed to traditional isolation problems considered in stationary WSNs. Activation of all the sensor nodes simultaneously is not necessarily energy efficient and cost-effective. In order to maintain self-adaptivity of the network, two algorithms, named Connectivity Re-establishment in the presence of Dumb nodes ( CoRD ) and Connectivity Re-establishment in the presence of Dumb nodes Without Applying Constraints ( CoRDWAC ), are designed. The performance of these algorithms is evaluated through simulation-based experiments. Further, it is also observed that the performance of CoRD is better than the existing topology control protocols—LETC and A1—with respect to the number of nodes activated, overhead, and energy consumption.

In this work, we propose a scheme, named CoRAD , for the reestablishment of lost connectivity using sensor nodes with adjustable communication range in stationary wireless sensor networks (WSNs), when “dumb” behavior occurs some of the nodes. Due to the occurrence of such behavior, there may be temporary loss of connectivity between among the nodes. Such a phenomenon is different from the commonly known node isolation problem in stationary WSNs. The mere activation of intermediate sleep nodes cannot guarantee reestablishment of connectivity, because there may not exist neighbor nodes of the isolated nodes. On the contrary, the increase in communication range of a single sensor node may make it die quickly. Including this, a sensor node has maximum limit of increase in communication range that may not be sufficient to reestablish connectivity. Therefore, considering all these factors for self-organization of the network and isolated node re-connection, we propose a price-based scheme, which addresses the issue by activating intermediate sleep nodes or by adjusting the communication range of some of the other nodes in the network. The scheme also deactivates the additional activated nodes and reduces the increased communication range when the dumb nodes resume their normal behavior, upon the return of favorable environmental conditions. To implement the proposed scheme, CoRAD it is required to construct the network using GPS-enabled adjustable communication range sensor nodes. Through simulation we compare our proposed scheme with the existing topology management schemes -- LETC and A1 -- in the same scenario by considering the number of activated nodes, message overhead, and energy consumption. We find that the proposed scheme shows improved performance compared to the existing topology management schemes.

In many developing countries, agriculture is one of the primary livelihoods of common people. Agriculture requires various types of technologies for improving crop yields. The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation. In this work, we propose a hardware prototype using Wireless Sensor Network (WSN) for intruder detection in an agricultural field. The proposed system is named Agricultural Intrusion Detection (AID). AID helps to generate alarms in the farmer's house and at the same time transmits a text message to the farmer's cell phone when an intruder enters into the field. In order to implement the proposed scheme, we design and deploy Advanced Virtual RISC (AVR) micro-controller-based wireless sensor boards over an outdoor environment and evaluate the performance.

In this work, we consider the presence of dumb nodes in the underlying physical networks of Sensor-Cloud infrastructure. The dumb nodes get isolated from the network and degrade the network performance. Consequently, a Cloud Service Provider (CSP) is unable to use Wireless Sensor Networks (WSNs) in an efficient manner, as the dumb sensor nodes cannot use as a virtual sensor. Thus, in this work, we propose a scheme, Connectivity Re-establishment in the Presence of Dumb Nodes in Sensor-Cloud Infrastructure (CoRDS), that facilitates the reestablishment of the connectivity between dumb nodes and the sink. Using the proposed scheme, CoRDS, the CSP is able to provide the services efficiently as per end-users requirements. In order to re-establish connectivity between the dumb nodes and the sink, we use a single leader multiple follower Stackelberg game. Additionally, theoretical characterization of CoRDS has been shown.