Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks †
Abstract
:1. Introduction
- The malicious SNs in the network are identified considering three factors: Forwarding Rate (FR), Response Time (RT) and Delayed Transmission (DT).
- A routing mechanism is proposed that ensures real time and energy efficient data delivery from SNs to BSs. The ANs act as relay nodes in the data delivery.
- Secure and reliable data delivery is ensured using the RSA technique.
2. Related Work
2.1. Trust Evaluation of Sensor Nodes
2.2. Nodes’ Authentication
2.3. Secure Routing in Networks
2.4. Lightweight Blockchain
2.5. Data Storage
Problems Already Addressed | Solutions Already Proposed | Validations Already Done | Problems to Be Addressed | C1 | C2 | C3 |
---|---|---|---|---|---|---|
Incorrect location estimation and energy dissipation | Node’s trust values are based on data based and behavioral based trust [10] | False Positive Rate (FPR), Detection Accuracy (DA), False Negative Rate (FNR), localization error, energy consumption | Malicious node detection consumes high computational cost. Due to indirect trust evaluation, nodes act maliciously | × | √ | × |
Existing models do not allow content access, reliable authentication and trust management | Blockchain authentication and trust module attains authentication and trust via digital signature [18] | N/A | Weak hashing algorithm. Poor authentication, malicious nodes tamper with the data | √ | × | × |
No traceability mechanism of nodes’ data fairness | BTM for malicious node detection is proposed which ensures traceability and transparency [19] | Security, traceability and reliability analysis | PoW requires high energy and faster computer processing to solve cryptographic puzzles that make it costly | × | √ | × |
SNs captured by malicious nodes broadcast inaccurate localization | Range free algorithm is proposed for secure localization [20] | Average localization error, localization error variance | Large communications overhead, consumes more energy due to the dynamic behavior of SNs | × | √ | × |
Security threats arise in IoT platform | IoT authentication protocol based on the blockchain is proposed [24] | N/A | Sink nodes do not authenticate the SNs at the time of assigning sequence numbers | √ | × | √ |
Dynamic WSN has more uncertainty and a large coverage area, which causes trust issues | Registration of nodes, cluster formation and node logout [25] | Forward and backward security, resistance to impersonation, storage overhead, energy consumption | Complexity increases in key management. Communication overhead between BS and high storage space sensors | √ | × | √ |
Lack of traceability of each node in the IoT network | IoT framework is proposed where tractability of each node requires nodes’ registration into the blockchain [26] | Probability of attack success, authentication accuracy | Requires extra maintenance cost and storage capacity. Data tampering in local database | × | √ | √ |
Secure socket layer does not ensure user anonymity | The proposed system ensures data authenticity using blockchain to store data [27] | Power consumption, temperature, humidity measurement | N/A | √ | × | × |
Network latency and data delivery issues occur due to mobile sensors | An intrusion prevention framework is proposed for mobile IoT devices to provide reliable data routing [28] | Network lifetime, Packet Delivery Ratio (PDR), energy consumption, delay and routing overheads | In XOR hashing function, if an attacker knows one of the plain texts, then get another through them | × | √ | √ |
Increase network overhead | Trust aware localized routing discovers multiple routes but selects one route with trusted SNs [29] | Security and throughput, encryption and decryption performance, time complexity | No authentication mechanism. Malicious nodes cause low packet delivery and high packet delay | √ | √ | × |
Trust issues and single point of failure due to the central authority | BCR protocol is introduced that enables trust relationship between IoT vendors and cooperators [30] | Throughput, PDR, route acquisition latency, routing overhead | Low PDR | × | √ | × |
Malicious nodes cause gray and black hole attacks | A routing scheme through blockchain and reinforcement learning is used [31] | Enhance the routing efficiency and security of WSNs | Expense and burden increased on the server side due to the operational complexity | √ | × | × |
Storage and bandwidth issues | A light chain system for resource constrained devices is proposed [32] | Hash operations, hash quality, throughput, storage cost | N/A | × | × | √ |
Distributed nature requires high storage and faster transaction | Multi-level architecture for handling the IoUT data is proposed [33] | Reliability, accuracy, total remaining energy, energy consumption | N/A | √ | × | √ |
Local copy of the blockchain records is not feasible | Aggregated information is used to reduce the communication cost [34] | Relative frequency, communication cost | N/A | × | × | √ |
Blockchain has a slow update rate, while, in Tangle, miners validate its two previous transactions before joining network | The authors presented an optimized policy by using Tangle and blockchain technologies for sampling rate [35] | Age of information and sampling interval | N/A | × | × | √ |
PoW requires high processing ability and data storage availability | Mobile edge computing framework is proposed to utilize the blockchain [37] | Total net revenue | N/A | √ | × | √ |
Nodes may behave selfishly, they do not forward the packet | An incentive mechanism encourages the nodes to store the data [38] | The proposed system reduced the computing power as compared to the PoW | No authentication mechanism, expensive data storage | √ | × | √ |
Blockchain requires high resources to perform PoW on mobile devices | Rolling blockchain is proposed where smart cars are used as the nodes of the WSN. The whole database is stored on the server [39] | Probability of finding the connected paths | Merkle tree is not utilized for this network | √ | × | √ |
High latency, scalability issues and single point of failure | Blockchain and SDN based hybrid architecture are used [40] | Hash rate, transactions per second, average time per block and latency | Credential information stored on SDN can be leaked | × | × | √ |
High computational cost and storage constraint due to a large number of IoT devices | SDN, edge, fog and blockchain are used to develop a secure attack detection system [41] | F1-score, detection time, detection rate, accuracy, bandwidth Matthews correlation coefficient | System complexity increased, requires high computational power, cloud causes high latency | × | × | √ |
The service provider offers malicious services to the client | A blockchain based fair nonrepudiation service provisioning mechanism is proposed [42] | Average gas consumption, average transaction latency, average throughput | No off-chain mechanism is mentioned to deliver the major service part | × | √ | √ |
No authentication, presence of malicious nodes, low PDR, high delay, usage of symmetric keys | A blockchain based authentication and trust evaluation mechanism is proposed for secure routing. RSA encryption scheme is used [Proposed Model] | Network lifetime, energy consumption, throughput, gas consumption, transaction latency, processing time of RSA encryption and processing time of trust evaluation | High time consumption in generating the RSA keys | √ | √ | √ |
2.6. Data Security and Privacy
2.7. Nonrepudiation Mechanism
3. Problem Statement
4. System Model
- All the ANs, SNs and BSs have a particular Ethereum address.
- All the BSs and ANs are legitimate.
- There are no external factors and harsh network conditions that can affect the objective parameters: DT, FR and RT.
- PoA consensus algorithm is used in the private blockchain for validation of transactions and adding the blocks into the blockchain.
- PoW consensus algorithm is used in the public blockchain to validate the transactions and add the blocks into the blockchain.
- Mutual authentication: When two nodes want to communicate with each other, they first need to be recognized before the interaction. The identity of all nodes is stored on the BSs that authenticate the ANs.
- Nonrepudiation: The nodes that take part in the communication cannot deny sending the packets. The nonrepudiation scheme is performed on the blockchain. All operations are stored on it, therefore, data tampering cannot be performed.
- Integrity: This includes the data packets’ integrity, where unauthorized nodes cannot access and illegally tamper with the data packets in the interaction process. The integrity of the data packets is ensured by the authentication process, which is carried out by the public and private blockchains.
- Transparency and traceability: The whole process is traceable and transparent because the information of SNs is bound to each data record. Whenever any malicious node exists in the WSN, it can be identified by the traceability feature of the proposed model.
4.1. Initialization
4.2. Registration
4.3. Authentication
Algorithm 1: Nodes’ registration |
4.4. Trust Evaluation Mechanism
Algorithm 2: Mutual authentication |
4.4.1. Delayed Transmission
Algorithm 3: Trust value evaluation of sensor nodes |
4.4.2. Forwarding Rate
4.4.3. Response Time
4.4.4. Node Communication Quality
5. Simulation Results
5.1. Simulation Setup
5.2. Performance Metrics
5.2.1. Packet Delivery Ratio
5.2.2. Network Lifetime
5.2.3. Residual Energy
5.2.4. False Positive Rate
5.2.5. False Negative Rate
5.2.6. Detection Accuracy
6. Formal Security Analysis
6.1. Integer Underflow and Overflow
6.2. Parity Multisig Bug 2
6.3. Callstack Depth Attack Vulnerability
6.4. Transaction Ordering Dependence
6.5. Re-Entrancy Vulnerability
6.6. Timestamp Dependency
7. Attacker Model
8. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
ANs | Aggregator Nodes |
API | Application Programming Interface |
BSs | Base Stations |
DA | Detection Accuracy |
DT | Delayed Transmission |
FR | Forwarding Rate |
FNR | False Negative Rate |
FPR | False Positive Rate |
IoT | Internet of Things |
MAC | Media Access Control |
NCQ | Node Communication Quality |
PDR | Packet Delivery Ratio |
PoA | Proof of Authority |
PoS | Proof of Stake |
PoW | Proof of Work |
RT | Response Time |
RSA | Rivest–Shamir–Adleman |
SNs | Sensor Nodes |
SDN | Software Defined Network |
WSNs | Wireless Sensor Networks |
Identity of an AN | |
Identity of a BS | |
Identity of an SN | |
Private Blockchain | |
Public Blockchain | |
Threshold to check the NCQ | |
Trust value of SNs | |
Weight of DT | |
Weight of FR | |
Weight of RT |
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Identified Limitations | Proposed Solutions | Validation Done |
---|---|---|
L1: Presence of malicious nodes | S1: Trust evaluation considering NCQ value to remove malicious nodes from the network | V1: Trust values of the SNs, FNR, FPR and DA. The results are depicted in Figure 2b and Figure 3a,b |
L2: Low PDR due to the involvement of malicious nodes L.3: High energy consumption of the SNs | S2, S3: The trusted SNs perform routing. SNs send their packets to the ANs, who forward the packets to BSs. Through this process, little energy is consumed by the SNs | V2, V3: PDR, network lifetime and residual energy. The results are depicted in Figure 2a and Figure 4a,b |
L4: Key exchange problem | S4: RSA is used for the secure transmission of data considering key generation, encryption and decryption | V4: Direct validation is not shown explicitly |
Parameters | Values |
---|---|
Sensing area | 100 × 100 m |
SNs | 100 |
ANs | 4 |
BSs | 2 |
Deployment | Random |
Initial energy of SNs | 0.05 J |
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Awan, S.; Javaid, N.; Ullah, S.; Khan, A.U.; Qamar, A.M.; Choi, J.-G. Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks. Sensors 2022, 22, 411. https://doi.org/10.3390/s22020411
Awan S, Javaid N, Ullah S, Khan AU, Qamar AM, Choi J-G. Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks. Sensors. 2022; 22(2):411. https://doi.org/10.3390/s22020411
Chicago/Turabian StyleAwan, Saba, Nadeem Javaid, Sameeh Ullah, Asad Ullah Khan, Ali Mustafa Qamar, and Jin-Ghoo Choi. 2022. "Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks" Sensors 22, no. 2: 411. https://doi.org/10.3390/s22020411