A survey paper on Trust Management for Internet of Things
K. Swathi K. Sugamya
Asst.Professor, IT Dept, CBIT, Hyderabad. Assoc.Professor, IT Dept, CBIT, Hyderabad
Abstract
The “internet of things” (IoT) conception is employed to outline or reference systems that trust the autonomous communication of a gaggle of physical objects. The applications degrees of the IoT are varied, including: smart-homes, smart-cities and industrial automation. IoT systems usually give nice edges to varied industries and society as a full. Trust management plays a crucial role in IoT for reliable knowledge synthesis and mining, qualified services with context-awareness, and increased user privacy and knowledge security. It helps individuals overcome perceptions of uncertainty and risk, and engages in user acceptance and consumption on IoT services and applications. However, current literature still lacks a comprehensive study on trust management in IoT. During this paper, providing a survey on the properties and objectives of IoT trust management, and additionally give a survey on this literature advances towards trustworthy IoT. Several of the IoT systems and technologies are comparatively novel, still several untapped applications areas, varied challenges and problems that require to be improved.
Introduction
The rising model of the Internet of Things (IoT) builds upon the cooperative property of smart-objects, so called smart objects, together with Radio Frequency identification (RFID) tags, sensors, actuators, PDAs, Mobile Phones, etc. with wide relevancy [1, 8]. The present work of IoT has been specialize in the design ofIoT and also the empowering technologies for seamless cooperation among smart-objects [1, 7-9, 19, 20, 23]. Additionally, researchers have developed vital IoT application situations, like e-health [3, 11], smart-home and smart-community [15]. because the building blocks of IoT, good objects with heterogeneous
characteristics would like hand and glove work along to accomplish the applying tasks. Another characteristic of IoT is that almost all smart-objects area unit human-carried or human connected devices. Therefore, the social relationships among the device users should be taken into thought throughout the planning part of IoT applications. Further, devices in IoT fairly often expose to public areas and communicate through wireless. Hence, IoT objects area unit prone to malicious attacks [17]. During this paper, we have a tendency to propose a trust management protocol for IoT considering each malicious and socially uncooperative nodes, with the goal to reinforcethe safety and increase the performance of IoT applications.
Trust management (TM) plays a vital role in IoT for reliable knowledge fusion and mining, qualified services with context-aware intelligence, and increased user privacy and data security. It helps individuals overcome perceptions of uncertainty and risk and engages user in acceptance and consumption on IoT services and applications. Trustcould be a sophisticated thought with relevancy the confidence, certainty, and expectation on the consistency, responsibleness, safety, ability, associate degreed alternative characters of an entity.Statuscould be a live derived from direct or indirect information or experiences on earlier interactions of entities associate degree is employed to assess the extent of trust place into an entity. However, the IoT poses variety of latest problems in terms of trust. Generally, IoT system contains 3 layers: a physical perception layer that perceives physical environments and human social life, a network layer that transforms associate degreed processes perceived surroundings knowledge and an application layer that provides context-aware intelligent services in a very pervasive manner. Every layer is in and of itself connected with alternative layers through cyber-physical social characteristics. A trustworthy IoT system or service depends on not solely reliable cooperation among layers, however conjointly the performance of the full system and every system layer with relevancy security, privacy and alternative trust-related properties. guaranteeing the trustiness of 1 IoT layer (e.g., network layer)doesn't imply that the trust of the full system is achieved. Unlike
other networking systems, new problems raised within the space of IoT caused by its specific characteristics.
First, knowledge assortment trust could be a crucial issue in IoT. If the collected gigantic volumes of knowledge from the physical perception layer aren't trustworthy enough, e.g., attributable to the injury or malicious input of some sensors, the IoT service quality are greatly influenced and onerous to be accepted by users albeit the network layer trust and also the application layer trust is totally provided.
Second, knowledge method trust ought to be ensured. Trustworthy knowledge fusion and mining needeconomical, accurate, secure, privacy-preserved, reliable and holographic knowledge method and analysis in a very holistic manner.
However, achieving all trust properties in IoT knowledge method is arduous task onerous to fulfill. Further, IoT services area supported knowledge method, analysis and mining. This truthtruly greatly interrupts user privacy. At the same time once the users relish advanced services they conjointlygot to disclose or got to share their personal knowledge or privacy. Showing intelligence providing context-awareassociate degreed personalized services and at an equivalent time conserving user privacy to an expected level introduces an enormous challenge in current IoT analysis and observe. Specifically, attributable to the cyber-physical and social characteristics of IoT, a way to offer trustworthy services through social computing could be a vitaluneasy topic. Within the literature, trust and reputation mechanisms have been wide studied in varied fields. However, current IoT analysis has not comprehensively investigated a way to manage trust in IoT in a very holistic manner. There's very little work on the trust management for IoT.
A number of problems, like huge knowledge / Big Data trust in assortment, process, mining and usage, user privacy preservation, trust relationship analysis, evolution and enhancement; user-device trust interaction, etc. haven't been extensively studied. IoT introduces further challenges to supply present and intelligent services with high qualification in observe, particularly once user privacy and knowledge trust ought to be seriously thought-aboutand strictly supported. During this paper, we have a tendency to study trust properties and propose the objectives of IoT trust management. we have a tendency to explore the literature towards trustworthy IoT so as to show variety of open problems and challenges and counsel future analysis trends associated with trust management. We have a tendency to additionally propose a model so as to realize comprehensive trust management in IoT and direct future research.
RELATED WORK
Establishing security communication channels supported trust and name models among sensing element nodes isa very important thought once coming up with a secure routing answer in IoT/CPS. ATRM [2] is an agent-based trust and reputation-based management theme for
WSNs wherever trust and reputation-based management is distributed domestically with nominal overhead in terms of additional messages and time delay. However, since mobile agents area unit designed to travel over the whole network and run on remote nodes,they need to be launched by trusty entities.An agent-based trust model for WSN is conferred in [4] employing a watchdog theme to look at the behavior of nodes and broadcast their trust ratings. Sensing element nodes receive the trust ratings from the agent nodes, that area unit liable for observance the previousand computing and broadcasting those trust ratings. In [14], a reputation-based theme known as DRBTS isprojected to produce a way by that beacon nodes, BN, will monitor one another and supply info so sensing element nodes, SN, will opt for who to trust, and based on a quorum approach. However, so as to trust a BN’s info, a sensing element should get votes for its trustworthiness from a minimum of 50% of their common neighbors. BTRM-WSN [18] could be a bio-inspired trust and reputation-model for WSN aimed to attain to the foremost.
trustworthy path resulting in the foremost honorable node in a very WSN giving an exact service. Every nodeshould maintain a secretion trace for every of its neighbors. CONFIDANT [19] is projected to increase reactive routing protocols with a reputation-based system so as to isolate misbehaving nodes. Every node monitors the behaviors of its next hop neighbors. Trust relationships and routing choices are supported knowledgeable, observed, or reportable routing and forwarding behavior of different nodes. SORI [20] theme is projected to encourage packet forwarding and disciplineegotistic behavior. The reputation of a node is quantified by objective measures, and therefore the propagation of nameis expeditiously secured by a one way-hash-chain-based authentication theme. Watchdog and Path rater mechanisms [16], area unit simply two extensions to the DSR rule. However, not all of the foremost familiar works stake into consideration the sturdy restrictions concerning process, storage or communication capabilities. a number ofthem trust a watchdog mechanism with or while not employing a multi-agent system. IoT/CPS assumes that trillions of things that used on a routine can eventually be connected to the Internet using 6LoWPAN [5] protocol and supply intelligent service through cooperating with one another.
Most things have the subsequent important characteristics [6] [10], restricted power capability, wireless receivers and transmitters with restricted vary facing the utilization of multi-hop communication, quality (things can move, presumably become disconnected) and violability (things could also be switched on and off frequently). All the on top of problems raise the requirement for the event of a completely unique management model, completely different from those being in use nowadays. Supported the analysis of characteristics of IoT/CPS and in-depth understanding of ATRM [2], ATSN [4], DRBTS [16], BTRM-WSN [18], friend [19], SORI [20] and WP [16], We tend to propose a completely unique trust and reputation-model TRM-IoT to enforce the cooperation between things in a network of IoT/CPS supported their behaviors.
System model of IoT
We consider an IoT system involving three layers, as illustrated in Fig. 1: a physical perception layer that contains a huge number of sensors, actuators, mobile terminals and sensor connectors and applies sensing technologies to sense physical objects (including human beings) and social environments by collecting huge amount of data in order to convert them into the entities in the cyber world; a network layer that includes all network components with heterogeneous network configurations (e.g., wireless sensor networks, ad hoc networks, cellular mobile networks and the Internet) for data coding, transmission, fusion, mining and analyzing at data processors in order to provide essential information to an
application layer that pervasively and intelligently offers expected services or applications to IoT end users. This system model is compatible with the reference architecture model proposed by EU FP7 IoT-A project, especially the IoT-A tree structure. Meanwhile, various cyber-physical social relationships exist crossing the above three layers, which can be explored and mined to offer advanced services for human-beings. IoT trust management is concerned with: collecting the information required to make a trust relationship decision; evaluating the criteria related to the trust relationship; monitoring and reevaluating existing trust relationships; as well as ensuring the dynamically changed trust relationships and automating the process in the IoT system.
Fig 1. A System model of IoT
Protocol for Trust management in IoT
Trust management is done by use of some protocol called as Trust Management
Protocol. For IoT Trust Management Protocol is distributed. Each node maintains its own trust assessment towards other nodes. For scalability, a node may just keep its trust evaluation towards a limited set of nodes which it is most interested in. The trust management protocol is encounter based as well as activity-based, meaning that the trust value is updated upon an encounter event or an interaction activity. Two nodes encountering each other or involved in a direct interaction activity can directly observe each other and update their trust assessments. They also exchange their trust evaluation results toward other nodes as recommendations. In this trust management protocol, a node maintains multiple trust properties in honesty, cooperativeness, and community-interest. The trust assessment of node i evaluating node j at time t is denoted by T_ij^X (t) where X = honesty, cooperativeness, or community-interest. The trust value T_ij^X (t) is a real number in the range of [0, 1] where 1 indicates complete trust, 0.5 ignorance, and 0 distrust. When node i encounters or directly interacts with another node k at time t, node i will update its trust assessment T_ij^X (t) as follows:
T_ij^X (t)={█((1-α) T_ij^X (t) (t-∆t)+αT_ij^(X,direct) (t),@if j==k; @(1-γ) T_ij^X (t) (t-∆t)+γT_ij^(X,recom) (t),@ if j!=k;)┤ —-(1)
Here, ∆t is the elapsed time since the last trust update. If the trustee node j is node k itself, node i will use its new trust assessment toward node j based on direct observations 〖(T〗_ij^(X,direct) (t)) and its old trust node j based on past experiences to update T_ij^X (t). A parameter α (0 ≤ α ≤ 1) is used here to weigh these two trust values and to consider trust decay over time, i.e., the decay of the old trust value and the contribution of the new trust value. A larger α means that trust evaluation will rely more on direct observations. Here, T_ij^(X,direct) (t) indicates node i’s trust value toward node j based on direct observations accumulated over the time period [0, t]. Below we describe how each trust component value T_ij^(X,direct) (t) can be obtained based on direct observations for the case in which node i and node j interacting or encountering each other within radio range:
T_ij^(honesty,direct) (t): This refers to belief node i that node j is honest based on node i ‘s direct observations toward node j. Node i estimates T_ij^(honesty,direct) (t) by keeping a count of suspicious dishonest experiences of node j which node i observed during [0,t] using a set of anomaly detection rules such as a high discrepancy in recommendation has been experienced, as well as interval, retransmission, repetition, and delay rules as in [21,22]. If the count exceeds a system-
defined threshold, node j is considered totally dishonest at time t, i.e., T_ij^(honesty,direct) (t)=0. Otherwise T_ij^(honesty,direct) (t) is computed by 1 minus the ratio of the count to the threshold. The hypothesis is that a compromised node must be dishonest. Consider non-zero false positive probability (Pfp) and false negative probability (Pfn) for such detection mechanism.
Similarly we can calculate the T_ij^(cooperativeness,direct) (t), T_ij^(Community-interest,direct) (t) which provides the degree of cooperativeness of node j as evaluated by node i based on direct observations over [0,t] and the degree of the common interest or similar capability of node j as evaluated by node i based on direct observations over [0,t] respectively.
On the other hand, if node j is not node k, then node i will not have direct observation on node j and will use its past experience T_ij^X (t-∆t) and recommendations from node k (T_ij^(X,recom) (t) where k is recommender) to update T_ij^X (t). The parameter γ is used here to weigh recommendations vs past experiences and to consider trust decay over time as follows:
-γ=(βT_ik^X (t) )/(1+ βT_ik^X (t) ) — (2)
Here introduced another parameter β ≥ 0 to specify the impact of “indirect
recommendations” on T_ij^X (t) such that the weight assigned to indirect recommendations is normalized to
βT_ik^X (t) relative to 1 assigned to past experiences. Essentially, the contribution of recommended trust increases proportionally as either T_ik^X (t) or β increases.
Conclusion and Future Work
The Internet has changed drastically the way we live, moving interactions between people at a virtual level in several contexts spanning from the professional life to social relationships. The IoT has the potential to add a new dimension to this process by enabling communications with and among smart objects, thus leading to the vision of ‘‘anytime, anywhere, anymedia, anything” communications. Thus, we observe that the IoT should be considered as part of the overall Internet of the future. We analyzed system model of IoT and trust management protocol of IoT. The protocol takes social relationships into account and advocates the use of three trust properties, honesty, cooperativeness, and community-interest to evaluate trust. The protocol is distributed and each node only updates trust towards others of its interest upon encounter or interaction events. The trust assessment is updated by both direct observations and indirect recommendations. We analyzed the effect of trust parameters (α and β) on trust evaluation. The results demonstrate that (1) using more new direct observations over pass information could increase the trust
assessment accuracy and trust convergence speed, and (2) using more indirect recommendations over pass information could increase the trust convergence speed but decrease the accuracy in the presence of false recommendation attacks from malicious nodes.
In the future, there is a possibility to develop trustee-based and mission-based trust management for IoT. There is requirement of dynamic trust management for IoT and explore new trust-based IoT applications that can adapt to changing environments such as malicious node population/activities
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