Internet of Things (IoT) is a network of devices that are connected through the Inter- net to exchange the data for intelligent applications. Though IoT devices provide several advantages to improve the quality of life, they also present challenges related to security. The security issues related to IoT devices include leakage of information through Differ- ential Power Analysis (DPA) based side channel attacks, authentication, piracy, etc. DPA is a type of side-channel attack where the attacker monitors the power consumption of the device to guess the secret key stored in it. There are several countermeasures to overcome DPA attacks. However, most of the existing countermeasures consume high power which makes them not suitable to implement in power constraint devices. IoT devices are battery operated, hence it is important to investigate the methods to design energy-efficient and secure IoT devices not susceptible to DPA attacks. In this research, we have explored the usefulness of a novel computing platform called adiabatic logic, low-leakage FinFET de- vices and Magnetic Tunnel Junction (MTJ) Logic-in-Memory (LiM) architecture to design energy-efficient and DPA securehardware. Further, we have also explored the usefulness of adiabatic logic in the design of energy-efficient and reliable Physically Unclonable Func- tion (PUF) circuits to overcome the authentication and piracy issues in IoT devices.
In the previous section, we discussed the gap between the IoT sensing devices and the cloud services. In this section, we elaborate on the problem of data forwarding and the need for reliable gateways between these two components, i.e., IoT devices and the cloud services. In fact, establishing a direct connection—without mediator hops—between the IoT devices and the smartphones highly reduces the bandwidth usage and energy consumption . In most cases, the sensing devices have limited wireless connection technologies due to their energy limitations where these devices are required to independently operate for a long time span . To achieve data exchange between the mobile gateways and the sensor nodes, there should be at least one wireless communication technology which will be used in both sections commonly. According to , the implemented software architecture for multi-standard and multi-technology inter-operation presents a reduced use of hardware resources in front of a relatively high-energy consumption, mostly due to the simultaneously active radio interfaces combined with small battery capacity, that limits the smartphone’s lifetime. To achieve an energy-efficient data exchange using wireless technology, the transition between sleep and wake up modes is highly required where the sleep mode usually is to save energy while no data exchange is needed.
Adiabatic logic is also used to design energy- efficient Physical Uncountable Function (PUF) . A survey on DPA countermeasures has concluded that adiabatic logic is one of the promising techniques to design low-power and securehardware . Further, the usefulness of adiabatic logic circuits for low-power and DPA resistant IoT devices is also established in a recent research article on ”Ultralow power and the New era of Not-So-VLSI” .With the emergence of IoT, there is an urgent need to design low-power and secure IoT devices. Improvement in the security of these devices comes at the cost of reduction in battery life. Battery life is considered as an important parameter in the design of self- powered IoT devices. Adiabatic logic is considered to be an alternate way to design low-power and DPA-resistant hardware. One of the main features of adiabatic logic is that it can operate efficiently at a frequency less than 1 GHz. Thus, adiabatic logic can be used to design low-power and secure
MOCRN [ 13 ] is another recent improvement of the LEACH protocol, which is based on the minimum separation distance between the cluster heads. Initially, MOCRN randomly selects K nodes as its cluster heads (CHs). Every node selected as a CH sends its information to the next node within its transmission range. The neighbor nodes that receive this information do likewise and transmit it to the next neighboring nodes, and the process continues, until the node meets a neighbor node contained in another cluster. Through this process, each local cluster is formed, and the number of clusters is the same as the number of cluster heads. Thus, choosing the number of CHs is the same as choosing the clustering size. In MOCRN, communication in the sensor network is divided into intra-cluster communication (using a single hop between cluster members and their cluster head) and inter-cluster communication (using multiple hops between cluster heads). MOCRN is a protocol that uses a simple process for the formation of clusters where CHs are selected based on the minimum distance between the nodes, but discounts the residual energy of the selected nodes, which may not have enough energy to receive, aggregate and re-transmit the data to the next CH or the base station. As described above, LEACH, LEACH-C, DECSA and MOCRN are energy-efficient protocols, which have been designed based on an energy-aware, but service-blind routing approach, where the sensor nodes/motes are assumed to have the same sensing and communication capabilities and also provide the same services. These routing algorithms are not fit for the next generation IoT, where the heterogeneity of services will lead to heterogeneous devices with different energy consumption profiles. Such a diversity of energy consumption profiles should be accounted for when designing next generation energy-efficient IoT platforms.
The routing process in 6LoWPAN starts when a reduced function sensor has to send a packet to another IP sensor. In this case, the higher full function sensor that is connected to the reduced function sensor will be re- sponsible to send the packet hop by hop until it reaches the gateway. Actually, the gateway uses the IP address to locate the domain of the remote IP sensor. Further, 6LoWPAN ad hoc on Demand Distance Vector Routing (Load) has been adopted for 6LoWPAN routing. Like AODV , Load uses route request and route reply messages but it does not use the sequence number. It also relies on the Link Layer Notification messages that approve the reception of Mac messages. It creates a mesh topology and runs on full function devices. A route is selected by Load if it includes a certain number of links such that their accumulated route cost (LQI)  is worse than a certain threshold. The route should also include less hops between the source and the destination. Hierarchical routing (HiLow) is another protocol that is used in 6LoWPAN to minimize the delay. The idea is to build a hierarchy and then 6LoWPAN device will either join an existing parent or become a parent. According to  6LoWPAN is used for networks with high processing capabilities. For this end, Protocol for Low Power and Lossy Networks (RPL)  has been designed for constraint devices in power, computation, and memory capabilities. RPL is a distance vector routing protocol that is based on IPV6. It builds a Destination Oriented Directed Acyclic Graph (DODAG). Many metrics may be used to construct a DODAG: the Expected Number of Transmissions (ETX) , the remaining energy of the devices… Energy-Efficient Probabilistic Routing (EEPR)  is an alternative solution for routing in an IoT environment. It is based on the same idea of AODV but the transmission of a RREQ packet follows a certain forwarding probability that depends on the residual energy and the ETX metric
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Inter- net of Things offers many sophisticated and ubiq- uitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy man- agement is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-pow- er devices and its related challenges. We detail two case studies. The first one targets energy-effi- cient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case stud- ies demonstrate the tremendous impact of ener- gy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.
Over the most recent couple of years, the world has seen real development of regular devices that are Internet- empowered, an idea usually alluded to as The Internet of Things (IoT). The Internet of Things is realized by a blend of minimal effort sensors, computing innovation, and networking which permit items, structures, and other framework to speak with one another and to be remotely gotten to by means of the Internet. We will keep on seeing an increasing speed of inter connectedness between the physical world and the advanced world; IDC predicts that by 2020 there will be an introduced base of 212 billion associated things, including 30.1 billion associated self-ruling things or things that can settle on choices in view of inherent tenets running locally or remotely (Lund, MacGillivray, and Turner 2013). In this paper, we will investigate how Machine-to- Machine (M2M) communications and IoT empowers us to push past vitality proficiency at the device level, into a significant new level of productivity at the frameworks level while preserving privacy and security and empowering new vitality administrations. We realize that Information and Communications Technology (ICT) is now driving productivity picks up crosswise over numerous industries and frameworks. In this paper, we will feature different free market activity side energy management applications, exhibiting how these advances deliver new potential outcomes in gathering continuous information which empowers new components, for example, prescient examination, energy load disaggregation, mechanization, and complex multi-dimensional upgrading calculations. We will highlight the part these advancements play in empowering new vitality proficiency, building dispatching, and request reaction benefits and incorporate bits of knowledge from research and exhibition tasks to demonstrate the effects of keen, associated devices and examination arrangements in private, business, and industrial applications.
One mechanism relies on preinstallation of this information. One example is Zig- Bee's high-security mode that assume that the network's master key is stored at manufacture time or at rmware-update time. This is infeasible for interop- erable IoT devices intended to be deployed by consumer, and as a consequence, no ZigBee device that we are aware of uses it. Another common example of preinstallation is the inclusion of prestored cryptographic certicates in main- stream operating systems (e.g., CA root certicates). This mechanism appears to be inappropriate for IoT devices because (1) there is no usable and reliable mechanism to invalidate/replace compromised certicates on IoT devices, (2) the mechanism requires public-key computations that are considered too expen- sive for ZigBee and Bluetooth Low Energy (BLE) devices, and (3) the stored certicates allow authentication of a named party in a protocol (e.g., domain name), but many IoT devices are nameless (some devices may be large enough to carry a printed identifying name, similar to a MAC address, but this is not a common practice). Even if the diculties with certicates are overcome, they only allow association if at least one of the devices has a rich enough interface to allow the user to specify or approve the name of the other device; this is not the case when two simple IoT devices need to be associated (e.g., a light bulb and a controlling switch). We that this mechanism is unsuitable for IoT.
To have a secure IoT-based system, considering a ref- erence IoT model is of pivotal importance. Without a reference IoT model it becomes quite difficult to have a global picture of an IoT-based system. Moreover, it becomes even more challenging to identify the levels where cryptographic schemes are required to be imple- mented. In the reference model of Fig. 1, security at the higher levels can be ensured through network firewalls and well-developed protocols. Moreover, the devices at higher levels normally operate in protected environments and they are well beyond the reach of malicious attack- ers. However, as we move towards the lower levels, the issue of security exacerbates and it becomes more chal- lenging to secure IoT devices. Specially from the gateway level onwards, we have to take into account the heteroge- neous nature of IoT devices which usually have different resource capacities and diverse throughput constraints. Furthermore, the lower level devices are usually in direct access of attackers which makes them an attractive target for all kinds of security attacks [16–19]. A few crypto- graphic countermeasures against these attacks have been proposed by [14, 24]. But these propositions are static
break through security (e.g., Equifax data breach  , OPM theft of 5.1 million fingerprints  , etc.). More often than not, communication between devices and servers in IoT applications is insecure (e.g., passwords transmitted in plain- text ) due to time-to-market constraints or inexperience in security imple- mentations. In an alternative instantiation (match-on card/device), the biometric information never leaves the card/device. However, the aforementioned physi- cal attacks can be used to gleam the template from the card/device or bypass critical security modules. Recently, homomorphic encryption has been proposed for securing biometric templates because it performs matching in the encrypted domain , . However, such approaches are still far from efficient, requiring several orders of magnitude extra hardware and operational time . Further, they still may be susceptible to attacks on the secret key kept on the user-side and fault injection at the decision module.
The Internet of Things (IoTs) is an integrated network including physical devices, mobile robots, cameras, sensors, vehicles, etc. There are many items embedded with electronics, software to support a lot of applications in different fields. These internet-based networks have many different types of data to be transmitted and processed. Either reducing data transmission or lowering energy consumption for such networks is critically considered. Compressed sensing (CS) technique is known as a novel idea to compress and to reconstruct correlated data well with a small certain number of CS measurements. This paper proposes an energy-efficient scheme for data routing for IoTs utilizing CS techniques. The ideas show how to apply CS into IoT applications with different kinds of data like images, video streaming and simply as sensor readings. After the CS sampling process, the IoT system only needs to transmit a certain number of CS measurements instead of sending all collected sensing data. At the receiver side, the system can reconstruct perfectly the original data based on the measurements. Different kinds of IoT data is analyzed to be used with CS. Data routing methods are suggested for suitable cases. Simulation results working on different types of multimedia data are provided to clarify the methods. This work also provides an additional way to protect the sensing data for security purposes in the networks.
Yet, an IoT system contains a large number of low- cost devices that may only be powered by batteries [6, 7]. These energy-limited devices, usually in the edge of a network , may cause cooperative communication to not work properly. Accordingly, some works introduce energy-harvesting (EH) technology to solve such restric- tion and achieve better energy efficiency [9, 10]. This technology can realize the reuse of energy resources. An SU with EH technology can harvest energy from the ambient environment [11, 12], e.g., solar energy, thermal energy, and sound energy. Additionally, it can also harvest energy from the surrounding electromagnetic field gen- erated by other communication nodes, which is defined as the wireless powered communication (WPC) tech- nology . The WPC-enabled SU is powered by radio frequency (RF) signals in the electromagnetic field and can convert the RF signals into a direct current that can be used in subsequent communications. As a result, the EH technology may increase the battery life of SUs by replenishing energy from various energy sources  and SUs can assist PUs without consuming additional
The number of IoT devices and applications are continuously growing leading to a significant increase in IoT data volume. ABI Research has estimated the IoT data volume to grow from 233 exabytes in 2014 to 1.6 zettabytes in 2020 . The different IoT devices and applications generating real-time data are dispersed over large geographical areas and support a variety of use cases and domains. A centralized computation and storage solution (e.g. cloud) for real-time heterogeneous IoT data is not ideal. IoT applications have strict requirements like high throughput during short time periods, very low latency, and prompt decision making based on real-time data analytics, which cloud computation cannot satisfy. With all the IoT devices and applications sending service requests to the cloud, it would be challenging to serve these requests in real-time resulting in inefficient service-provisioning and increased latency. Additionally, IoT ecosystems are constrained in terms of low power communications, scarce energy, and lossy communications, which necessitates localized computation and storage solutions for processing, analyzing, and storing IoT data [97-101].
_____________________________________________________________________________________________________ Abstract— Internet of Things (IoT) allows people and things to be connected anything, anytime, anyplace and anyone ideally by using network. In IoT environment the massive devices makes the forwarding of data from source to sink is more critical. So packets should be routed efficiently in the network by reducing congestion, packet loss and delay rate. The redundant deployment of network equipment reduces the network utilization, which will degrade the energy efficiency of networks. In many of the IoT applications, often involves battery powered nodes which are active for a long period, without any human control after the initial deployment. The improper use of routing techniques is the common reasons of drain out the battery within few days. So there is a need to develop some new energyefficient routing algorithms and protocols. This paper focuses on the energyefficient routing mechanism using link quality based clustering algorithm. The proposed method provides reduced energy consumption, maximizing the network coverage, less packet loss and reduced delay rate.
Edge-of-Things (EoT) has emerged in the progressing world of information technology, as a paradigm liable to provide utmost quality of processing and storage ser- vices at edge of the network. The Internet of Things (IoT) is progressing rapidly offering an infrastructure that is most viable to bring a significant quality enhancement to human life and productivity . However, the grow- ing advancements in this technology demands a platform that is more energy-efficient and causes lesser delay. The need to process and store huge volumes of data at a fast pace is the major concern for leveraging resources. IoT devices backbone has two major infrastructures, cloud and fog/edge computing. Both of them significantly pro- vide efficient data storage and resource management, enabling the developers to create interactive Internet ser- vice platforms for smart access.
Wireless communication systems play the major role in activating IoT. Wireless systems connect the sensor devices to IoT gateways and perform end-to-end data communications between these elements of IoT. Wireless systems are developed based on different wireless standards and the use of each one depends on the requirement of the application such as communication range, bandwidth, and power consumption requirements. For example, often renewable sources of energy, including wind and solar power plants are mostly located in very remote areas. Therefore, ensuring a reliable IoT communications in remote places is challenging. Employing IoT systems on these sites requires selection of suitable communication technology that can guarantee a continues connection link and support real-time data transfer in an energyefficient manner. Due to the importance of communication technologies in IoT, in this subsection we review some of these technologies. We also indicate to few examples to show their role in the energy sector. Then, we provide a comparison in Table 1 to show the difference of each of the technologies when applied with IoT.
Abstract— IOT exemplify energetic international system foundation together with autonomic composing skills according to approved conformity intelligence rules of conduct in which bodily including digital chattels get identities, peculiarities, personal individuality with brilliant consolidations moreover flawlessly incorporated into the knowledgeable arrangement of connections. Devices harvest liveliness in distinction to surrounding provenience. Taking advantage of renew chances and modulating accomplishment guidelines stand on ongoing and envisioned spirit elevation, harvest links acquire likely to cope with contradictory depiction aims of life-span along with attainment. The papery has shown that the use of harvesting features of IOT devices are ignored in existing literature. The use of multiplexing is not considered by the existing researchers. The effect of multiplexing is not considered by majority of researchers. Indicated issues are overthrown by late technique proposed hereby. The new technique will utilize compression and multiplexing to reduce the energy consumption rate, therefore will improve overall network lifetime.
Chapter 3 and Chapter 4 focus on architecture design for standard compliant IoT plat- forms. Chapter 3 describes a configurable SDR baseband processor. Several reasons, such as multi-standard scenarios, on-the-fly updates, and graceful data rate/distance trade-offs, can be handled easily by SDR as long as the overhead of SDR can be kept small compared to overall system. We show this is the case for our processor in this chapter. Better en- ergy efficiency can be achieved by operating the SDR at an energy optimal configurations which can be beyond the standard for existing physical layer protocols. Chapter 4 further augments this benefit from baseband flexibility by introducing information coding flexibil- ity in IoT communications. As mentioned in the previous section, in addition to energy efficiency and flexibility, security is also one of the most critical challenges posed for IoT wireless connectivity. The Galois Field (GF) processor proposed in Chapter 4 addresses the security challenges with a unified architecture to perform not only error correction coding but also popular cryptography functions in a finite GF for both data encryption/decryption and authentication processes.
The programming of IoT boards has traditionally required an extensive back- ground on hardware and software components of the target device. This has resulted in a steep learning curve for the developers in this environment. For example, con- sider someone who wants to develop a temperature measurement system using an Arduino-based IoT device. To do so, the person needs to know how to program in the C language as well as be acquainted with hardware-related concepts regard- ing measurement sensors and the Arduino-based IoT device. Moreover, if the same person wants to transfer the application to run on another IoT device such as a Raspberry Pi, this person will need to port the code into this new devices pro- gramming platform by re-writing the application in another programming language (e.g., Python). In addition to the cost of learning a broad range of programming languages and platforms for different IoT devices, vital IoT device functions and operations such as over-the-air and serial programming, Bluetooth and WiFi data transmission protocols, and IoT device management require great attention and an in-depth knowledge of IoT devices. PROVIZ+ is designed with an easy all-in-one programming feature to mend this dissonance. This feature allows users to program any of these boards using only the Panther language.
Building energy-efficient cryptosystems has been the primary focus of my research thus far. However, many of the circuit techniques commonly employed to reduce power and energy consumption actually expose an inherent tradeoff between energy and security: side-channel attacks. For example, power-based side-channel attacks gather information about power consumption of a device over time to deduce the secret key and other protected information, and since IoT devices are physically exposed to attackers, this is an important threat to address. Simply implementing theoretically proven crypto-algorithms is not enough for this domain. The future research would investigate ways to build energy-efficient cryptosystems that are also protected against invasive and noninvasive side channel attacks.